Monthly Archives: November 2023

Entire genome sequences for nearly half a million people have been released by the UK Biobank, representing the largest dataset of its kind in the world.

The resource has the potential to offer new insights into the causes of major common diseases and guide the choice of potential therapeutic targets.

It has hailed as a step change in genomics and is available to approved researchers around the world through the UK Biobank Research Analysis Platform.

This is a veritable treasure trove for approved scientists undertaking health research, and I expect it to have transformative results for diagnoses, treatments and cures around the globe, said UK Biobank principal investigator Sir Rory Collins, PhD.

Executive vice president for innovative medicine research and development at industry partner Johnson & Johnson John Reed, PhD, maintained the findings could pave the way for more efficient clinical development and drive progress towards precision medicine.

This landmark dataset will enable us to leverage the power of artificial intelligence and machine learning for rapidly identifying novel disease targets and helping researchers predict how a candidate medicine might impact certain subpopulations of patients, based on their genetics, he said.

The UK Biobank whole genome sequencing (WGS) consortium was formed in 2018 with the goal of sequencing the genomes of all UK biobank participants.

The five-year project cost 200m, involved 11 partners and took 350,000 hours of sequencing time to create 27.5 petabytes of genetic data. At its peak, over 20,000 whole genomes, each with around three billion base pairs of DNA, were being sequenced each month. It resulted in the genomes of 491,554 UK Biobank volunteers being sequenced overall.

Half the funding came from the U.K. government and the Wellcome research organisation. The remaining 100 million was given by the biopharmaceutical and healthcare companies Amgen, AstraZeneca, GlaxoSmithKline, and Johnson & Johnson.

In return for their 25m investment, each of the four companies received a nine-month head start with the data before its public release.

The large-scale biomedical database and research UK Biobank resource follows the health of half a million volunteers recruited in 2006 and has already provided numerous clinical insights.

Data collected on over 10,000 variables, including blood pressure, cognitive function, diet and bone density, have been studied to examine why having the same genetic predisposition for a disease can result in different outcomes, reactions and side-effects to identical treatments.

It has led to thousands of scientific studies being published, and major insights such as the discovery that Type 1 diabetes is as common in adults as children.

Executive vice president of research and development at Amgen David Rees, PhD, said: This ground-breaking dataset allows scientists to explore how genetics affect levels of proteins, metabolites and other physiological factors, more closely than ever before, promising to accelerate our understanding of the genetic underpinnings of disease.

Chief executive of UK Research and Innovation (UKRI) professor Dame Ottoline Leyser, PhD, noted: Researchers can now apply to access de-identified full genome data from half a million participants, alongside a rich combination of medical, biochemical, lifestyle and environmental data from volunteers involved.

Today marks an important milestone in UKRIs commitment to realise the potential of genetics for biomedical research, innovation and translation to the clinic.

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"Ground-Breaking" Release of World's Largest Whole Genome Resource - Inside Precision Medicine

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Pangenome analysis reveals genomic variations associated with domestication traits in broomcorn millet - Nature.com

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Global genetic diversity, introgression, and evolutionary adaptation of indicine cattle revealed by whole genome ... - Nature.com

Viral metagenomic analysis

The number of clean reads was 21,157,543 for the RNA sample and 26,789,502 for the DNA sample. For RNA, the data were assembled to a total sequence length of 2,337,534, with 60.92% GC content. The length of the largest contig was 11,556 nt, which was identified as APPV (Table1), and named as APPV-SDHY-2022 for further analysis in this study. For DNA, the data were assembled with a total sequence length of 38,447,346 and 41.71% GC content. Other viruses, including Getah virus, porcine picobirnavirus, porcine kobuvirus, porcine sapovirus, Po-Circo-like virus, porcine serum-associated circular virus, porcine bocavirus 1, porcine parvovirus 1, porcine parvovirus 5 and porcine circovirus 3 were also identified by sequence alignment ((Table1), however, most contigs of these viruses were less than 500bp (see Additional file 2: Table s2 & Table s3). No other known pathogens (PRRSV, PPV2-4/68, CSFV, PCV2 and Japanese encephalitis virus) related to abortion were sequenced.

APPV presence was confirmed in the pooled sample by RTPCR amplification targeting the NS3 gene (see Additional file 3: Fig.s1A). The assembled sequence of the PCR products was identical to that of APPV-SDHY-2022 (see Additional file 3: Fig.s1B). This provided additional evidence of APPV presence in the abortion cases.

The genome of strain APPV-SDHY-2022 (GenBank accession no. OP381297) contains 11,556 nucleotides (nt) and consists of a 5UTR (370 nt, positions 1 to 370), CDS (10,909 nt, 371 to 11,279), and 3UTR (277 nt, 11,280 to 11,556). The nucleotide and amino acid sequences of the individual proteins of the strains were aligned separately, and the homology between APPV-SDHY-2022 and the reference strains was determined (Table2). Sequence alignment based on APPV polyprotein CDS showed that the nucleotide identities of APPV-SDHY-2022 with Clade I, Clade II, and Clade III strains were 82.6-84.2%, 93.2-93.6%, and 80.7-85%, respectively, while the amino acid identities were 91.4-92.4%, 96.4-97.7%, and 90.6-92.2%, respectively. APPV-SDHY-2022 shared the highest nucleotide identity (93.6%) with APPV-China/GD-SHM/2016, and the highest amino acid identity (97.7%) with GD-YJHSEY2N. Among the 12 mature proteins, NS5A showed the lowest homology (77.6-93.3% at the nt level) with the reference strains.

Phylogenetic analysis was performed based on complete polyprotein CDS and NS5A nucleotide sequences. The results showed that APPV-SDHY-2022 belongs to a separate branch of Clade II (Fig.2A). Moreover, the results revealed that the homology of NS5A nucleotide sequences was above 94.6% for the same isoform, 84.7-94.5% for different isoforms of the same clade and 76.8-81.1% for different clades (Table3). Therefore, we proposed that Clade II strains can be further divided into three subclades and that APPV-SDHY-2022 belongs to subclade 2.3. APPV-China/GD-SD/2016 and APPV-China/GZ01/2016 belong to subclade 2.2, and the other Chinese strains among the Clade II cluster belong to subclade 2.1 (Fig.2B). Since Clade II strains were found only in China, this typing method can help us better analyze the evolution of Clade II strains.

Phylogenetic analysis of Chinese APPV strains. Phylogenetic trees based on the nucleotide sequences of the complete polyprotein CDS (A) and the NS5A gene (B) were constructed by the neighbor-joining (NJ) method with 1,000 bootstrap replicates in MEGA11 software. The APPV-SDHY-2022 strain reported in this study is indicated with a red dot

To further explore the genetic evolution of APPV, potential recombination events were identified using Recombination Detection Program version 4 (RDP4) and then examined using SimPlot version 3.5.1. Among all available APPV strains, 8 strains (GD-DH01-2018, GD-BZ01-2018, JX-JM01-2018A01, GD2, GD-HJ-2017.04, GD-LN-2017.04, GD-CT4, and GD-MH01-2018) had potential genetic recombination events. Although NGS of APPV-SDHY-2022 confirmed recombination events of JX-JM01-2018A01 and GD-HJ-2017.04 by RDP4 (see Additional file 4: Table s4), no obvious genetic recombination in APPV-SDHY-2022 strains was observed by SimPlot software in this study (Fig.3).

Recombination analysis of the complete genomes of the APPV-SDHY-2022 strain from Shandong Province. Potential recombination events were identified using Recombination Detection Program 4 (RDP4) and then examined using similarity plots and bootstrap analysis in Simplot 3.5.1. The major and minor parents were JX-JM01-2018A01 and GD-HJ-2017.04, respectively

Amino acid sequences of individual viral proteins of all the Chinese APPV strains were analyzed. No amino acid insertions or deletions were found in the APPV-SDHY-2022 strain. The amino acid sequences of the individual proteins were compared to identify those that differentiate Clade II from Clade I and Clade III, and 20 unique amino acids were found in Clade II strains (Fig.4), among which, most sites were distributed on NS5A(7H,16A,69Q,131Q,152M,189I,280A,397F,437A) and NS5B(77V,139P,193P,231K,274A), and the remaining sites were on Npro (85D,120E), C(90K), Erns(91K,139Y) and NS3(30T). Interestingly, the amino acids at these unique sites were identical between Clade I and Clade III strains, demonstrating that it is possible to determine the type of strain by measuring these specific amino acids alone.

The unique amino acids found in Clade II APPV strains. Amino acid sequences of viral proteins were aligned with reference strains using MEGA11 and BioEdit software

In this study, putative N-glycosylation sites in the three important glycoproteins, Erns, E1, and E2, in Chinese APPV strains were also predicted. APPV-SDHY-2022, along with most of the strains in Clade II, is heavily glycosylated, with a total of ten N-glycosylation sites (N104 in the E1 protein; N12, N26, N43, N64, and N99 in the Erns protein; N51,N64,N103, and N127 in the E2 protein) (Fig.5). All the Chinese APPV strains had a conserved putative N-glycosylation site at N104 with a consensus N-I-T motif in the E1 protein. The putative N-glycosylation sites in the Erns and E2 proteins differed greatly among strains in different subclades, and 9 patterns of putative N-glycosylation sites were observed in E2 proteins, including N51+N64+N103, N64+N103, N51+N64+N103+N141,N51+N64+N127+N103+N141,N51+N64+N103+N127,N64+N103+N127,N51+N127,N51+N64,N64(Fig.5). Among the N-glycosylation sites of E2 proteins, a putative site at N64 was highly conserved.

Putative N-glycosylation sites of Erns, E1 and E2 proteins. The putative N-glycosylation sites within the Erns, E1 and E2 sequences of Chinese APPV strains were predicted according to a glycosylation analysis algorithm, and are shown as a blue shaded box

To analyze the effect of glycosylation sites on the antigenicity of the E2 protein, the antigenic index was determined by the Jameson-Wolf method in this study, and the results showed that aa positions at 1~9, 15~28, 34~44, 49~55, 62~82, 118~130, 136~158, 174~184, 188~196 and 200~205 of the E2 protein were the potential immunodominant regions. A comparison of the antigenic index within Chinese strains with and without a specific putative site showed that the putative N-glycosylation site at N51 had a negative effect on the antigenicity of the corresponding region (Fig.6).

Antigenicity prediction for the E2 protein. The Jameson-Wolf algorithm, which combines secondary structure information with backbone flexibility to predict surface accessibility, was used to determine the predicted antigenic index, with a threshold value of 1.7. The putative N-glycosylation sites within the E2 sequences of Chinese APPV strains are shown as a blue arrow. Representative strains from different Clades/subclades or patterns of putative N-glycosylation sites were included, and the strains in each subclade with different patterns of putative N-glycosylation sites are underlined

To further analyze the effect of glycosylation sites on conformational epitopes of the E2 protein, BepiPred-3.0 was used to predict B-cell conformational epitopes. The results showed that the 15 most likely B-cell conformational epitope residues varied among different Clades/subclades or patterns of N-glycosylation sites, and 39E, 70R, 173R, 190K, and 191N were conserved residues among all Chinese strains (Table4) (see also the graphical representations of the predicted epitopes in Fig.7).

Conformational B-cell epitope prediction for the E2 protein. The potential B-cell conformational epitopes of the E2 protein in APPV Chinese strains were predicted by BepiPred-3.0, and the residues with scores above the threshold (default value is 0.1512) are predicted to be part of an epitope and colored in yellow on the graph (where Y-axes depict BepiPred-3.0 epitope scores and X-axes protein sequence positions). Shown is the graphical output of B-cell discontinuous epitope predictions for the E2 protein with APPV-SDHY-2022 as an example

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Genome characteristics of atypical porcine pestivirus from abortion cases in Shandong Province, China - Virology Journal - Virology Journal

Unusual low-quality ONT genomes due to extensive modifications

We sequenced 12 microbial strains of Listeria monocytogenes using Illumina and ONT R9.4 flowcells (~200990Mbp, SUP model) (Fig.1a, Supplementary Tables1 and 2). The ONT reads were assembled into genomes with sequencing errors further polished by Medaka and Homopolish (Supplementary Table3, see Methods). The Illumina and ONT read were hybrid assembled for evaluation purposes (Supplementary Table4). When compared with the Illumina/ONT hybrid assemblies (Fig.1b), seven ONT-only genomes exhibited high quality (HQ) ranging from Q47 to Q60 (e.g., R19-2905 and R20-0088). However, five isolates (R20-0026, R20-0030, R20-0127, R20-0148, and R20-0150) showed unexpectedly low quality (LQ) varying from Q26 to Q32. The accuracy of these five LQ genomes remained unimproved after replicated ONT sequencing. Further investigation of the five LQ genomes revealed excessive amounts of mismatch errors (15335670) compared with the seven HQ ones (040 mismatches) (Fig.1c). Homopolymer errors (i.e., indels) were not the source of inferior quality (7306, Supplementary Table5).

a Workflow of ONT-only and ONT/Illumina hybrid assembly; b Q scores; c number of mismatches (red: LQ, gray: HQ); d comparison of ONT and Illumina reads by IGV; e numbers of 5mC, 6mA, and mismatches between HQ/LQ strains (n=12, red: LQ, gray: HQ). Error bars represent the minimum and maximum values.

Manual inspection revealed that these mismatches were ONT basecalling errors uncorrected after genome polishing (Fig.1d and Supplementary Fig.1). As mismatch errors in ONT are mainly due to epigenetic modifications, we computed the frequency of well-known methylation in these isolates (see Method and Supplementary Table6). In terms of 5-methylcytosine (5mC), the numbers of modified loci in the five LQ genomes (~240340k) were not significantly higher than those in the HQ ones (210345k, P=0.89, Fig.1e). Similarly, the numbers of N6-methyladenine (6mA) modifications also showed no significant difference between the LQ and HQ groups (98218k vs. 126223k, P=0.34). Because the numbers of mismatch errors in LQ genomes are significantly higher than those of HQ ones (P=0.005), we suspected ONT basecalling algorithms failed to distinguish the novel modification types in the LQ isolates.

We removed the modifications in all microbial samples by WGA (Fig.2a), which randomly amplifies the genome fragments without retaining any epigenetic modification (see Methods). The WGA-demodified samples were sequenced by ONT (R9.4), assembled into chromosomes, and compared with the Illumina/ONT hybrid genomes (Fig.2a, Supplementary Tables7 and 8). The five LQ genomes after WGA exhibited significantly higher quality than those without demodifications (e.g., Q26 to Q53 in R20-0026) (Fig.2b, Supplementary Table9). In particular, the amounts of mismatch errors significantly reduced after demodification (e.g., 5670 to 16 in R20-0026) (Fig.2c). Consequently, the unexpected low quality of ONT was due to excessive modification-induced errors untrained in their basecalling model. The demodification by WGA can produce high-quality ONT genomes without the need for Illumina short reads.

a Worflow of WGA-demodified ONT; b Q scores of the WGA-demodified and ONT-only genomes (gray: ONT, black: WGA ONT); c numbers of mismatches of the WGA-demodified and ONT-only genomes (gray: ONT, black: WGA ONT); d WGA and ONT-only genome quality with respect to sequencing depth (shading: mininum and maximum quality in five replicates, line: median quality); e numbers of active/available pores during WGA-demodified and ordinary ONT sequencing.

However, while WGA successfully erased these modifications, the sequencing cost increased by two factors. First, WGA required a higher sequencing depth (~100) for assembling a complete genome when compared with ordinary ONT sequencing (~30) (Fig.2d and Supplementary Figs.2 and 3). It was due to the uneven amplification of WGA, which led to non-uniform sequencing depth and a fragmented assembly at moderate coverage. Second, the WGA-demodified samples may reduce the ONT yields. We observed the number of available/active pores could sometimes decrease quickly (e.g., less than 100 pores after 12h) (Fig.2e), which was possibly owing to the hyperbranched structure unresolved after WGA10. Consequently, the sequencing cost of WGA-demodified samples using ONT is much higher than ordinary sequencing.

We developed a novel computational method (called Modpolish) for correcting these modification-mediated errors without WGA and prior knowledge of the modification systems. Modpolish identifies and corrects the modification-mediated errors by leveraging basecalling quality, basecalling consistency, and evolutionary conservation (Fig.3a, see Methods). Briefly, because the ONT signals are disturbed by modifications, the basecalling quality is substantially lower than the modification-free loci (Supplementary Fig.4). As such, the basecalled nucleotides are often inconsistent at the modified loci (Supplementary Fig.5), yet these loci are within conservative motifs (Supplementary Fig.6). In conjunction with the conservation degree measured by closely-related genomes, only the modified loci with ultra-high conservation will be corrected by Modpolish, avoiding false corrections of strain variations with high specificity.

a Workflow of Modpolish; b Q scores before and after Modpolish; c numbers of mismatches before and after Modpolish (gray: before Modpolish, black: after Modpolish); d the antiviral defending systems encoded by the 12 strains (gray: before Modpolish, black: after Modpolish); e the sequence motif of modification sites in the four mza-encoding strains; f the sequence motif of modification sites on the R20-0026 strain.

We assessed the accuracy of Modpolish by comparing the quality of the ONT-only genomes (polished by Medaka) with those further polished by Modpolish. The results indicated that Modpolish significantly improved the quality of all LQ genomes from Q2734 to Q60 (Fig.3b, Supplementary Table10). The number of mismatches also greatly decreased (e.g., from 5670 to 67 in R20-0026) (Fig. (3c). The numbers of mismatches in some HQ genomes were also reduced by Modpolish. For instance, the mismatches in the R19-2905 were erased from 40 to 6. Consequently, our results suggested that Modpolish made no false corrections on the HQ genomes (Supplementary Tables1113). The comparison of different basecaller versions and models (v4.0.14 vs. v6.3.4, HAC vs. SUP) indicated that these errors remain exist and Modpolish successfully erases most of them (Supplementary Fig.7).

As the modification systems often involve anti-phage defense (e.g., R-M, BREX, DISARM)11,12,13, we investigated the defending systems possessed by the HQ and LQ strains (Fig.3d) (Supplementary Data1). All the HQ genomes encompass at least one R-M system (e.g., Type I, II, or III), which is missing in all LQ isolates. Instead, four LQ strains (i.e., R20-0030, R20-0127, R20-0148, R20-150) carry a novel methyltransferase-encoding mza defending system which is absent in all HQ genomes (Supplementary Fig.8). Analysis of modification sites of the four mza-encoding LQ strains revealed pentanucleotide motif GCAGC (Fig.3e, Supplementary Fig.6). On the other hand, modification loci in the LQ R20-0026 all centered on the motif GCTGG (Fig.3f). Together, these results suggested that two lineage-specific modification systems extensively edited the five LQ genomes. Although their underlying mechanisms remained unclear, the editing at specific motifs with high conservation within each lineage allowed cost-effective in silico correction of these errors by Modpolish.

We then assessed the performance of Modpolish on public ONT datasets sequenced by R9.4 (SUP) and R10.4 flowcells (SUP, duplex/simplex modes). In the R9.4 dataset14, we first compared the quality of seven bacterial genomes polished by Medaka and Modpolish (Fig.4a, Supplementary Table14). The quality of five genomes significantly improved from ~Q45 to Q60. Similarly, the improvement was mainly due to the reduction of mismatches (Fig.4b). For instance, the number of mismatches decreased from 388 to 13 in the Staphylococcus genome after Modpolish. On average, the mismatch reduction rates of all genomes ranged from 50-96%. Consequently, although these bacterial genomes are not extensively modified, Modpolish can further improve their quality after Medaka without false corrections.

Comparison of Medaka and Modpolish for a Q scores and b mismatches on the R9.4 dataset; comparison of Medaka and Modpolish for c Q scores and d mismatches on the R10.4 dataset.

In the R10.4 (duplex mode) dataset3, we compared the genome qualities polished by Medaka and Modpolish (downsampled to ~60) (Fig.4c, Supplementary Table15). In general, Modpolish made little or no improvement in the duplex dataset. For instance, the mismatches produced by Modpolish only reduced from 20 to 19 on the Bacillus genome (Fig.4d). The overall genome quality is very high such that no differences can be seen (Q60). Modpolish demonstrated marginal on a recently published simplex dataset (R10.4, kit 14, Dorado v0.1.1) (Supplementary Fig.9). Therefore, the qualities of ONT R10.4 flowcells, in particular the duplex mode, is not only higher than those of R9.4 and require nearly no further correction. On the other hand, Modpolish may be used to fill the accuracy gap between simplex and duplex modes when the projects aim for higher throughput.

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Correcting modification-mediated errors in nanopore sequencing by nucleotide demodification and reference-based ... - Nature.com

Researchers at the Gladstone Institutes have developed a CRISPR-based genome shredding technique that shows promise in treating glioblastoma, an incurable brain cancer. Their research was published this week in the journal Cell Reports.

Much of the work done to develop the technique was done in the lab of Jennifer Doudna, PhD, an author on the paper, and co-winner of the 2020 Nobel Prize in Chemistry for the discovery of CRISPR-Cas9 gene editing technology. Other key players are Mitchel Berger, MD, a neurosurgeon and director of the Brain Tumor Center at University of California, San Francisco (UCSF), whose team helped secure patient-derived cell samples, and Alexendar Perez, MD, PhD, a resident at UCSF who performed much of the computational analysis needed for the study.

Computational analysis was necessary for diving into the non-coding portions of the genome to identify repetitive sequences shared by the glioblastoma cells. Cancer treatments rarely kill all tumor cells. In glioblastoma and other highly recurrent cancers, tumor cells that escape treatment develop multiple genetic mutations that allow them to proliferate.

Building on prior research, the Gladstone team surmised that mutated glioblastoma cells have a unique genetic signature that could be targeted. According to the paper, the team identified unique recurrent GBM-specific sgRNAs mainly in the non-coding genome that were generated by TMZ [chemotherapy] signature mutations characteristic of hypermutated gliomas. Those sequences are the beacon that guides CRISPR to the cancerous cells where it cuts up them up leading to genome fragmentation and DNA damage-induced cell death.

There is a lot to do before this CRISPR-based genome shredding technique can be used therapeutically. For example, the researchers noted that there are inefficiencies in the delivery modalities that need to be addressed. And it is important to note that the work published in Cell Reports does not detail a path to direct clinical implementation for this approach. But the results are promising evidence of CRISPRs potential to treat not just glioblastoma but other hypermutated tumors, according to Christof Fellmann, PhD, study lead and corresponding author on the paper. We see CRISPR as a gateway to a new therapeutic approach that wont be subject to the possibility of tumor cell escape.

And the researchers have reason to be hopeful. Results in the paper indicate that the technique works only on the tumor cells, sparing healthy ones during treatment. And in cases where tumor cells escaped the initial shredding, they succumbed to a second round of treatment. We understand so much today about glioblastoma and its biology, yet the treatment regimens havent improved, said I-Li Tan, PhD, first author on the paper. Now we have a precise way to target the cells that are driving the cancer, and we hope this may one day lead to a cure.

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CRISPR-Based "Genome Shredding" Technique Shows Promise in Treating Glioblastoma - Inside Precision Medicine

Construction of the VgrG database

Encoded as a stand-alone gene or fused at the N-terminus of the toxin, the MIX domains can assist the delivery of their cognate T6SS effector19,20. As the central component of the spike complex, VgrG is a good marker to explore the potential conserved domains involved in the delivery of T6SS effectors. Therefore, we set out to create a comprehensive dataset of VgrG proteins from available Gram-negative genome sequences lodged in the public GenBank database.

Previous studies have revealed that the Afp8 proteins of extracellular contractile injection systems (eCISs) are homologous to VgrG proteins, thus representing a potential confounding influence on the integrity of the dataset24,25,26. Therefore, we firstly downloaded 872 experimentally verified VgrG proteins from the established SecReT6 T6SS database27. It provides a positive control dataset to better avoid potential false positive hits (such as Afp8 homologs). A bioinformatic scan for conserved domains confirmed that the VgrG domain (accession: COG3501) was present in all 872 verified VgrG proteins in addition to 472 Afp8 proteins available from the dbeCIS database26. Importantly, the identified domains found in 861 (99%) verified VgrGs range between 451 and 750 amino acids, whereas there are only 10 (2%) Afp8 proteins that fall within this size range (Fig.1a). We therefore proposed the use of an empirical criterion for the further systematic screening for bona fide VgrG proteins in the 133,722 publicly available bacterial genomes (Fig.1a). Using this approach, a total of 130,825 VgrG proteins were successfully identified from 45,041 Gram-negative bacterial genomes.

a The workflow for the identification of valid VgrGs from 133,722 publicly available bacterial genomes. The 872 VgrGs available from the established T6SS database SecReT6 (red) and 472 putative Afp8 proteins, encoding VgrG domains, available from the eCIS database dbeCIS (green) were used as positive and negative datasets respectively for the selection of the empirical criteria for large-scale VgrG screening. b The 872 VgrGs available from the SecReT6 database with predefined subtype information are indicated by colored stars (key). VgrGs from subtypes i4a and i5 were mixed within the same clade in the tree, but these two subtypes were indeed closely related in the previous study27. The known type iii T6SS clade, derived mostly from Bacteroidetes, is highlighted with red shadow.

To further characterize the VgrG proteins identified above, we constructed a maximum-likelihood (ML) phylogenetic tree based specifically on the sequences of the conserved VgrG domains (Fig.1b). Using the aforementioned 872 previously defined VgrGs as indicators, we observed that our ML tree exhibited a similar overall topology regarding types/subtypes of T6SS operons as previously described27, supporting the validity of our approach.

Firstly, a screen was performed to identify MIX containing protein, based on the aforementioned VgrG database. A total of 7208 MIX containing proteins within vgrG loci were identified, which are widely distributed among various bacteria (Supplementary Fig.1). Importantly, sandwiched between vgrG and downstream effector gene, MIX domain exhibit multiple encoding configurations including single proteins and fusions at the C-terminus of VgrG or N-terminus of effector (Supplementary Fig.2).

Based on the encoding features of MIX domain, we then developed a screening strategy to identify more conserved domains with similar multiple encoding configurations as MIX within vgrG loci from the VgrG database created above (Fig.2). In brief, we scanned a maximum of three downstream genes of each vgrG locus to collect the conserved domains within the proteins sandwiched by vgrG and downstream toxin (if present). A domain family was reported if it was present in both of two encoded forms: stand-alone gene (i.e., single form) and fused to either the C-terminus of VgrG or the N-terminus of a toxin (i.e., fusion form). Finally, to further explore the presence of these domain families within vgrG loci in finer detail, we extended our search without the limitation of linkage to known toxins to identify more candidate domain-containing proteins within vgrG loci (Fig.2).

For each vgrG locus, a maximum of three continuous downstream genes encoded on the same strand as vgrG, with an intergenic distance between adjacent genes of <1kb were collected. Known components of the T6SS operon and any annotated pseudogenes were excluded. Then, the 280,581 remained downstream genes were scanned for conserved domains by batch CD-search. A total of 1321 putative toxin domain families were deduced from a collection of 928 experimentally verified exotoxins/effectors available from the VFDB database53. Each domain family identified within downstream genes dataset were further classified into three cases for final manual curation and determination.

After the screening process and careful manual curation, DUF2345 (cl01733), FIX-like (cl41761), LysM (cl21525), 5 (cl33691), PG_binding_1 (cl38043) and PHA00368 (cl30808) were successfully identified (Supplementary Table1). As shown in Supplementary Fig.3, besides the single form, all these domain families have at least one fusion form. Further, the FIX-like (cl41761), LysM (cl21525), 5 (cl33691) and PG_binding_1 (cl38043) families can be found in both fusion forms. Notably, some of them were encoded adjacent to known T6SS adaptor, which implies that their functions can be different from T6SS adaptors.

Besides MIX domain, three well characterized T6SS adaptor families (DUF4123, DUF2169, and DUF1795) had been reported to assist the interaction between VgrG and its cognate effectors. We further screened these adaptor families encoded within vgrG loci. Amongst 130,825 vgrG loci, besides three adaptor domains (37.44%) and MIX domain (3.14%), 31.33% of vgrG loci encode at least one of the six conserved domain families identified here. In contrast, only 28.09% of vgrG loci do not include any of the adaptor/MIX/conserved domains mentioned above (Supplementary Fig.4).

Although DUF2345 is considered as an extension of the VgrG gp5 domain, it is not encoded by all VgrGs6,28,29. Nevertheless, among the aforementioned six conserved domains, the DUF2345 domain is the most frequently identified in vgrG loci (Supplementary Table1). We therefore explored its function in T6SS. Three vgrG loci encoding the DUF2345 domain were found in Escherichia coli PAR, Pseudomonas aeruginosa strain PAO1 and PS42 (Fig.3a). Sequence comparison indicated that AKO63_2953 (VgrGPAR), AKO63_2954 (DUF2345PAR) and AKO63_2955 (M35PAR), corresponding to the VgrG domain, the DUF2345 domain and the M35 (metallopeptidase) toxin domain of PA0262 (VgrG2bPA), respectively. Similarly, Q094_05019 (VgrGPS) of P. aeruginosa PS42 encodes VgrG domain, whereas Q094_05020 encodes N-terminal DUF2345 domain and C-terminal M35 domain. AlphaFold v2.0 predicted that VgrGPAR, VgrGPS and VgrG domain of VgrG2bPA have the same conformation (Supplementary Fig.5a). Further, E.coli locus (VgrGPAR, DUF2345PAR and M35PAR), PS42 locus (VgrGPS and Q094_05020) and VgrG2bPA form similar trimmer structure, which implies that these three complexes might endow similar biological functions (Supplementary Fig.5b). As these three loci encode VgrG, toxin and immunity proteins, we speculate that DUF2345 maybe involved in the interaction between VgrG and its cognate effector.

a The vgrG loci of E. coli PAR, P. aeruginosa PAO1 and PS42. b E. coli expressing VgrG2bPA or its truncated mutant VgrG2bPAM35 were detected by Western blot. Anti-RpoB is lysis control. c Survival of E. coli expressing VgrG2bPA or its truncated mutant VgrG2bPAM35 in pET22b. Ten-fold serial dilutions of cultures were spotted on LB agar containing the stated concentrations of IPTG and grown for 24h. The image is representative of three independent experiments. d Intraspecies P. aeruginosa competition assay between the VgrG2bPAPA0261 strain and various isogenic attacker strains at 37C for 24h. Competition assay between the parental strain (PAO1) and itself (gray) is the internal control. The values and error bars represent the meanSD (n=3 biological replicates). A one-way ANOVA with Dunnetts test was employed using the parent versus prey competition as the comparator (*p<0.05; ns, not significant). e E. coli expressing M35PAR, AKO63_2955-2956 or DUF2345PAR were detected by western blot. Anti-RpoB is lysis control. f Survival of E. coli expressing M35PAR, AKO63_2955-2956 or DUF2345PAR in pET22b. Ten-fold serial dilutions of cultures were spotted on LB agar containing the given concentrations of IPTG and grown for 24h. The image is representative of three independent experiments. g Interactions between DUF2345PAR and VgrGPAR or M35PAR. Shown here are immunoblots of lysates (total) and immunoprecipitates with anti-FLAG affinity beads (IP: FLAG) of DUF2345PAR transformed with empty vector or a plasmid encoding Myc-tagged VgrGPAR or S-tagged M35PAR. GFP and VgrGPRE are control proteins. h DUF2345PAR mediates the interaction between VgrGPAR and M35PAR. Shown here are immunoblots of lysates (total) and immunoprecipitates with an anti-FLAG affinity beads (IP:FLAG) of M35PAR transformed with a plasmid encoding either Myc-tagged VgrGPAR or S-tagged DUF2345PAR.

Wood et al. showed that VgrG2bPA-PA0261 constitutes a T6SS antibacterial effector-immunity pair30. E. coli toxicity assay was used to test whether the DUF2345 domain in VgrG2bPA is toxic to bacteria (Fig.3b, c). As expected, overexpressed in E. coli, VgrG2bPA exhibited acute toxicity and co-expression of the immunity gene (PA0261) relieved this growth defect. Crucially, truncation of the M35 domain of VgrG2bPA restored growth, which indicated that DUF2345 in itself is not toxic to E. coli. Intraspecies P. aeruginosa competition assays were also performed to determine whether the DUF2345 domain could affect the function of VgrG2bPA (Fig.3d). Although the VgrG2bPAPA0261 strain exhibited a significant growth disadvantage against the wildtype PAO1 strain, it could no longer be outcompeted by both ClpV2PA and VgrG2bPA attacker strain. Notably, compared with the wildtype vgrG2bPA gene, the complement of vgrG2bPADUF2345 could not restore the growth advantage of the attacker strain. Further, although the secretion of Hcp (the T6SS inner stylet protein) was not affected, complemented in the VgrG2bPA strain, VgrG2bPADUF2345 could only be detected in the cells, but not in the supernatant (Supplementary Fig.6a). In addition, the production of VgrG2bPADUF2345 was still detrimental to E. coli when it remains in the periplasm (Supplementary Fig.6b, c). Therefore, it is clear that the DUF2345 domain disturbs the antibacterial ability of VgrG2bPA by ablation of its secretion.

We subsequently explored the function of DUF2345 when encoded as a distinct gene, which is within the locus containing vgrGPAR, M35PAR, along with the cognate immunity protein (Fig.3a). E. coli toxicity assay demonstrated that M35PAR exhibited bacterial killing activity, which was inhibited by its immunity protein (Fig.3e, f). Consistent with the results of Fig.3c, expression of DUF2345PAR in isolation had no deleterious effect on bacterial growth (Fig.3f). Immunoprecipitation assays of proteins co-expressed in E.coli confirmed that DUF2345PAR can specifically bind VgrGPAR and M35PAR, but not VgrGRPE (VgrG in Burkholderia sp. RPE67) (Fig.3g). Importantly, M35PAR could not interact with VgrGPAR in the absence of DUF2345PAR (Fig.3h). These results implied that DUF2345PAR is involved in the interaction between VgrGPAR and M35PAR to assist the loading of M35PAR on the T6SS spike.

Taken together, DUF2345 domain is indispensable for the delivery of its cognate toxin via fusion at the C-terminus of VgrG or encoded as a single gene.

Considering that DUF2345 is encoded as either a fusion at the C-terminus of VgrG or a distinct gene downstream of vgrG, we then investigated whether the sequences of VgrG domains showed a correlation with those of DUF2345. An iterative procedure was devised to hierarchically cluster the 52,277 VgrG domains and their cognate DUF2345 domains, respectively. At the 30% amino-acid sequence similarity cutoff, VgrG domains form three major clusters and ten outliers, whereas DUF2345 domains were classified into 37 distinct groups (Supplementary Fig.7). These findings imply that, compared to the relatively conserved VgrG domains, the sequences of DUF2345 domains exhibited higher diversity.

As we demonstrated above, DUF2345 is involed in the interaction between VgrG and the toxin protein. To further delve into this, we performed a Sankey analysis to investigate the relationship between DUF2345 domains and their downstream toxins in greater detail. It is interesting to note that most of DUF2345 clusters showed an obvious taxon-specific distribution and correlated well with their downstream toxins (Fig.4). Meanwhile, we also noticed that there are some toxins which correlated to more than one of DUF2345 clusters, such as Lyz-like and DUF2235 domains. To test whether this is a result of the intrinsic sequence diversity of these toxins, an iterative procedure was applied to further subdivide these toxin groups. As expected, the sub-clusters of Lyz-like and DUF2235 domains also correlated well to DUF2345 groups (Supplementary Fig.8). Thus, our data reveals that, DUF2345 domains exhibit high sequence diversity andcorrelate well with their downstream toxins.

A Sankey diagram showing the relationship between bacterial phylum/class, family, the corresponding DUF2345 clusters and the downstream toxin domain families (from left to right). Only DUF2345-encoding loci with adjacent known toxin domains were included. Loci from genomes without necessary taxa information were excluded. The number of sequences involved in each node is given after the node name. The red arrows on the right indicate some toxins which were linked to more than one DUF2345 clusters.

Absent from T6SS, LysM containing protein is one of the core components of eCIS, which shares several key homologous proteins in common with T6SS and forms a similar architecture31,32. Therefore, it is fascinating that our systematic screening implied that LysM domain is likely to be functional in T6SS.

Figure5a showed a vgrG loci encoding a LysM containing protein in Ketobacter alkanivorans GI5. E. coli toxicity assay showed that Kalk_10455 exhibited acute toxicity and co-expression of Kalk_10450 relieved this growth defect, which indicated that Kalk_10450 is an immunity protein against Kalk_10455 (Fig.5b, c). Notably, Kalk_10465 (VgrGG15) and Kalk_10460 (LysMG15) exhibited no toxicity when they were expressed in E. coli (Fig.5c). Although immunoprecipitation assays of proteins co-expressed in E.coli confirmed that Kalk_10455 specifically binds LysMG15 and VgrGG15, Kalk_10455 could not bind VgrGG15 in the absence of LysMG15 (Fig.5d).

a The vgrG loci of Ketobacter alkanivorans GI5 and Burkholderia sp. RPE67. b Immunoblots demonstrating the expression of VgrG2bG15, LysMG15 and Kalk_10455 in E. coli. Anti-RpoB is lysis control. c Survival of E. coli expressing VgrGG15, LysMG15 and Kalk_10455 in pETduet. Ten-fold serial dilutions of cultures were spotted on LB agar containing the stated concentrations of IPTG and grown for 24h. The image is representative of three independent experiments. d Interactions between VgrGG15, LysMG15 and Kalk_10455. Shown here are immunoblots of lysates (total) and immunoprecipitates with anti-FLAG affinity beads (IP: FLAG) of Kalk_10455 and GFP transformed with a plasmid encoding Myc-tagged VgrGG15 or Strep-tagged LysMG15. 0423PA is control protein. e Immunoblots demonstrating the expression of BRPE_05220 and NLPC_P60 domain in E. coli. Anti-RpoB is lysis control. f Survival of E. coli expressing BRPE_05220 and NLPC_P60RPE domain in pETduet. Ten-fold serial dilutions of cultures were spotted on LB agar containing the stated concentrations of IPTG and grown for 24h. The image is representative of three independent experiments. g LysM domain mediates the interaction between VgrGRPE and BRPE_05220. Shown here are immunoblots of lysates (Input) and immunoprecipitates with an anti-FLAG affinity beads (IP:FLAG) of BRPE_05220 or BRPE_05220LysM transformed with a plasmid encoding either Myc-tagged VgrGRPE or Myc-tagged 0423PA. 0423PA is control protein.

BRPE67_05220 in Burkholderia sp. RPE67, which includes both LysMRPE and NLPC_P60RPE domain, was used to further explore the function of LysM domain (Fig.5a). E. coli toxicity assays demonstrated that BRPE_05220 exhibited bacterial killing activity. Moreover, expression of NLPC_P60RPE domain in isolation had deleterious effect on bacterial growth, which was inhibited by BRPE_05230 (Fig.5e, f). Further, immunoprecipitated wildtype BRPE_05220, but not LysM truncated in BRPE_05220 (BRPE_05220LysM), interacted with BRPE_05210 (VgrG RPE) (Fig.5g).

AlphaFold v2.0 predicted that BRPE_05210 (VgrG) and BRPE_05220 (LysM and NLPC_P60) form similar trimmer structure with VgrG2bPA, which implied that LysM may mediate the interation between VgrG and toxin (Supplementary Fig.9). Further, the LysM domain phylogenetic analysis revealed the diversity of T6SS-related LysM domains, which is evolutionarily distinct from the phage-/eCIS-associated LysM domains (Supplementary Fig.10).

In sum, encoded at downstream of LysM containing gene or fused at the C-terminal of LysM domain, toxin interacts with VgrG in a LysM dependent manner implying LysM may assist the loading of its cognate effector onto the secretion apparatus.

The DUF2345 containing proteins exhibit specific correlation with their downstream diverse toxins (Fig.4). A similar Sankey analysis was performed to investigate the relationship between the other five identified conserved domain families along with the confirmed co-effector (MIX) and their downstream toxins (Supplementary Fig.11). Notably, most of the characterized toxin domains showed an obvious domain specific distribution with limited exceptions. For instance, as polymorphic toxins, RHS-containing proteins encode variable C-terminal toxic domains with conserved N-terminal RHS domain13. Most of the Rhs superfamily are linked to FIX-like (cl41761) and 5 (cl33691) domains. LysM domains are mainly correlated with Lyz_like, NlpD and NLPC_P60 superfamilies. As these domain families identified in this study, including FIX-like (cl41761), LysM (cl21525), 5 (cl33691), PG_binding (cl38043) and PHA00386 (cl30808), share a similar genetic organization and correlation with downstream toxins as the DUF2345 domain, it is reasonable to speculate that they would also function in the T6SS effector discrimination.

The overall distribution of the six conserved domain families was then analyzed (Fig.6). It is interesting to note, these families were not evenly encoded among different bacterial families. For example, although DUF2345 domains are widely distributed among Proteobacteria genomes, they are rarely encoded in the genomes of Vibrionaceae and Rhodospirillaceae bacterial families. In contrast, the PG_binding_1 domain is limited to the genomes of -proteobacteria, including the families of Chromatiaceae, Sinobacteraceae and Vibrionaceae. In general, although these conserved domains are widely encoded among various bacteria, their distributions exhibit obvious taxonomic specificity, which is coincident with their corresponding cognate effectors as shown in Fig.4 and Supplementary Fig.11.

Only taxa with genomes encoding at least one of the six conserved domains within the vgrG loci are shown for brevity. A total of 55,228 vgrG loci are included, but genomes without known assigned genus are excluded. The circles represent phylum, class, order, family and genus from inner to outer, and are color-coded by phylum/class (key). The family names are given outside the taxonomic tree. The outer heatmaps represent the percentage of genomes encoding the corresponding conserved domains for each genus (key).

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Genome wide analysis revealed conserved domains involved in the effector discrimination of bacterial type VI secretion ... - Nature.com