Category Archives: Genome

Transformative alliance will help advance the World Health Organization's strategy to end the global TB epidemic by 2035

SAN DIEGO, Oct. 18, 2022 /PRNewswire/ -- Illumina Inc. (NASDAQ: ILMN), a global leader in DNA sequencing and array-based technologies, and GenoScreen, an innovative genomics company, today announced a partnership to accelerate progress to end tuberculosis (TB) worldwide. The partnership will expand capabilities for countries most impacted by tuberculosis to more effectively detect and combat multidrug-resistant TB (MDR-TB). This alliance will enable global access to a package combining Illumina sequencing products and the GenoScreen DeeplexMyc-TB assay, a targeted next-generation sequencing (NGS) based test for rapid and extensive detection of anti-TB drug resistance, to promptly inform treatment decision. This will help advance the World Health Organization's (WHO) strategy to end the global TB epidemic by 2035.

"Through our partnership, we will enable lower-income countries to confront the pervasive threat of TB and work toward eliminating it," said Phil Febbo, chief medical officer of Illumina. "The COVID-19 pandemic response led to expanded NGS capacity around the world, so now institutions have the platforms needed to support testing for TB drug resistance and improve survival for patients with TB, the leading infectious disease killer prior to COVID."

According to the WHO, TB is the first worldwide bacterial infectious killer, claiming over 1.5 million lives each year. And even though TB can be cured when appropriately treated, MDR-TB represents a global public health emergency. In 2019, an estimated 465,000 people developed TB with rifampicin resistance (RR) or MDR, yet only 40% of these cases were detected and enrolled on MDR-TB treatment. Due to the COVID-19 pandemic in 2020, deaths from TB increased for the first time in a decade.

"As a world specialist in TB genomic solutions, we envision this partnership with Illumina as an accelerator for the global deployment of our DeeplexMyc-TB assay, especially for countries with the highest needs," said Andr Tordeux, CEO of GenoScreen.

Standard culture-based testing methods currently incur turnaround times of up to two months, and conventional molecular assays are limited in identifying drug resistance. The combined use of the GenoScreen DeeplexMyc-TB assay and the Illumina NGS platformsallows much more rapid determination of extensive drug resistance profiles and TB strain types affecting patients.

The DeeplexMyc-TB assay, developed and produced by GenoScreen since 2019,usesa culture-free approach to identify TB mycobacteria and more than 100 non-TB mycobacterial species, and to predict resistance to 15 antibiotics, in 24 to 48 hoursdirectly from primary respiratory samples. The Deeplex web application for automated analysis of the sequencing data enables clinicians to easily interpret the results and best define personalized treatments.

"The DeeplexMyc-TB kitis the most comprehensive commercial molecular test for detection of anti-TB-drug resistance available to date," said Philip Supply, research director at the French National Centre for Scientific Research. "We are continually updating the technology to detect emerging resistance to the newest anti-TB drugs."

Implementing NGS testing will also benefit national TB programs around the world by providing critical surveillance data about resistance to different anti-TB drugsimportant information for high-burden countries to guide TB control strategies.

Use of forward-looking statementsThis release may contain forward-looking statements that involve risks and uncertainties. Among the important factors to which Illumina's business is subject that could cause actual results to differ materially from those in any forward-looking statements are challenges inherent in developing, manufacturing, and launching new products and services, and Illumina's ability to successfully partner with other companies and organizations to develop new products, expand markets, and grow its business, together with other factors detailed in Illumina's filings with the Securities and Exchange Commission, including its most recent filings on Forms 10-K and 10-Q, or in information disclosed in public conference calls, the date and time of which are released beforehand. Illumina undertakes no obligation, and does not intend, to update these forward-looking statements, to review or confirm analysts' expectations, or to provide interim reports or updates.

About IlluminaIllumina is improving human health by unlocking the power of the genome. Our focus on innovation has established us as a global leader in DNA sequencing and array-based technologies, serving customers in the research, clinical, and applied markets. Our products are used for applications in the life sciences, oncology, reproductive health, agriculture, and other emerging segments. To learn more,visitillumina.comand connect with us onTwitter,Facebook,LinkedIn,Instagram, andYouTube.

Investors:Salli Schwartz858-291-6421[emailprotected]

Media:Samantha BealUS: 714-227-2661[emailprotected]

GenoScreen Contact:Pierre Burguire[emailprotected]

SOURCE Illumina, Inc.

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Illumina and GenoScreen Partner to Expand Access to Genomic Testing for Multidrug-Resistant Tuberculosis - PR Newswire

BARCELONA, Oct. 18, 2022 An interdisciplinary team from the Centre for Genomic Regulation (CRG) and the Institute for Research in Biomedicine (IRB Barcelona) has developed an imaging technique that captures the structure of the human genome to reveal how individual genes fold at the nucleosome level the fundamental units constituting the genomes three-dimensional architecture. The technique integrates superresolution imaging with advanced computational modeling.

According to the researchers, the method allowed them to image the structure of the human genome at unprecedented resolution. They believe the technique could have a long-term impact on scientific discovery.

Scientists used the technology, called Modeling immuno-OligoSTORM (MiOS), to create and virtually navigate 3D models of genes. Since almost every human disease has some basis in genes, the ability to visualize how genes work could lead scientists to a better understanding of how genes affect the health of the human body. The developers of MiOS hypothesized that taking superresolution microscopy and merging it with advanced computational tools could be a way to image genes at the level of detail necessary to study their shape and function, so as to fully understand their function and regulation.

MiOS showed the distribution of nucleosomes within specific genes in superresolution, through the simultaneous visualization of DNA and histones. It integrated this information with restraint-based and coarse-grained modeling approaches. It allowed quantitative modeling of genes with nucleosome resolution and provides information about chromatin accessibility for regulatory factors such as RNA polymerase II.

According to researcher Juan Pablo Arcon, the method provided a picture, or movie, of the 3D shape of genes at resolutions beyond the size of nucleosomes, reaching the scales that are needed to understand in detail the interaction between chromatin and other cell factors.

We show that MiOS provides unprecedented detail by helping researchers virtually navigate inside genes, revealing how they are organized at a completely new scale, researcher Vicky Neguembor said. It is like upgrading from the Hubble Space Telescope to the James Webb, but instead of seeing distant stars well be exploring the farthest reaches inside a human nucleus.

In the future, observations on genetic information obtained through MiOS could be used to catalog variations in the shape of genes that cause disease, for example. MiOS could also be used to test drugs that may be able to treat a disease by changing the shape of an aberrant gene.

The researchers intend to develop MiOS further, adding additional functionality that can, for example, detect how transcription factors (i.e., proteins involved in the process of transcribing DNA into RNA) bind to DNA.

The research was published in Nature: Structural & Molecular Biology (www.doi.org/10.1038/s41594-022-00839-y).

Originally posted here:
Superresolution Method Poised to Better Gene Function Understanding - Photonics.com

Subject recruitment

Subjects for this cross-sectional study were recruited based on recall from a community-based multi-ethnic prospective cohort27 that is part of the Singapore Population Health Studies project (SPHS -formerly Singapore Consortium of Cohort Studies). This subset included 109 subjects who were 48 to 76 years old with 65 males and 44 females (Supplementary Data1). Subjects in SPHS were recruited to participate in the National Health Survey, where subjects were selected at random using age- and gender- stratified sampling to obtain a representative sample set of residents in the country. During the period of recruitment from April 16th, 2008 to September 20th, 2018, subjects did not have any pre-existing major health conditions (cardiovascular disease, mental illness, diabetes, stroke, renal failure, hypertension and cancer) based on self-reporting27. The ethnicity of each subject was confirmed verbally so that all four grandparents of the subject belonged to the same ethnic group. As such, we do not anticipate that any self-selection bias was introduced. A separate comparison of baseline clinical measurements was performed, including age-adjusted BMI and HbA1c, against the rest of the subjects in the larger ethnicity-specific cohorts within Singapore Population Health Studies to ensure that the sampling for the initial cohort conformed to population norms. Informed consent was obtained from all participants. Each subject was given 60 Singapore Dollars for their participation in this study. All associated protocols for this study were approved by the National University of Singapore Institutional Review Board (IRB reference number H-17-026) on May 9th, 2017 and renewed until May 31st, 2021.

Fecal samples were collected from healthy subjects using the BioCollectorTM kit (The BioCollective, Colorado, USA). Samples were double-bagged and transferred to a polystyrene box, together with a pre-chilled ice-pack (20C). The polystyrene box was transferred to a cardboard box and later collected from the participants home within the same day. Samples brought to the Temasek Life Sciences laboratory were stored into an anaerobic chamber (atmosphere of N2 (75%), CO2 (20%), and H2 (5%)). Fecal samples were homogenized and subsamples transferred into sterile 2mL centrifuge tubes.

Genomic DNA was extracted from fecal material (0.25g wet weight) using the QIAamp Power Fecal Pro DNA kit (QIAGEN GmbH, Cat. No. 51804) and was quantified using Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific, Cat. No. Q32853). Integrity of the extracted DNA was verified using 0.5% agarose gel electrophoresis.

Metagenomic libraries were prepared with a standard DNA input of 50ng across all samples, using NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs, Cat. No. E7805), according to the manufacturers instructions. The reaction volumes were, however, scaled to a quarter of the recommended volumes for cost effectiveness. Barcoding and enrichment of libraries was carried out using NEBNext Multiplex Oligos for Illumina (96 Unique Dual Index Primer Pairs; New England Biolabs, Cat. No. E6440). Paired-end sequencing (2151bp reads) was carried out on the Illumina HiSeq4K platform with a minimum and average depth per sample of 2.4Gb and 9.4Gb respectively.

Purity and integrity of DNA was assessed and ensured to fall within recommended ranges before library preparation. To preserve the integrity of DNA, the shearing step was omitted and DNA was used directly for DNA repair and end-prep. Single-plex libraries were prepared using 1D sequencing kit (Oxford Nanopore Technologies, SQK-LSK108 or SQK-LSK109) according to the 1D Genomic DNA by ligation protocol. For samples that were multiplexed (12-plex), the native barcoding kit (Oxford Nanopore Technologies, EXP-NBD103 or EXP-NBD104 and EXP-NBD114) was used and libraries were prepared according to the Native barcoding genomic DNA protocol. Both native barcode ligation and adapter ligation steps were extended to 30min instead of 10min. Single-plex samples were sequenced on either the MinION or GridION machine with either FLO-MIN106D or MIN106 revD flowcells. Multiplex samples were sequenced on the PromethION machine with FLO-PRO002 flowcells. Raw reads were basecalled with the latest version of the basecaller available at the point of sequencing (Guppy v3.0.4 to v3.2.6). Basecalled nanopore reads were demultiplexed and filtered for adapters with qcat (v1.1.0 https://github.com/nanoporetech/qcat). The minimum and average sample depth was 1.2 and 4.7Gb respectively. Number of reads ranged from 300,000 to 3.4 million (average=1.4 million).

Hi-C libraries were generated using Phase Genomics ProxiMeta kit (version 3.0), based on the standard protocol. Briefly, 500mg fecal material was crosslinked for 15min at room temperature with end-over-end mixing in 1mL of ProxiMeta crosslinking solution. Once crosslinking reaction was terminated, quenched fecal material was rinsed. Sample was resuspended and a low-speed spin was used to clear large debris. Chromatin was bound to SPRI beads and incubated for 1h with 150L of ProxiMeta fragmentation buffer and 11L of ProxiMeta fragmentation enzyme. Once washed, beads were resuspended with 100L of ProxiMeta Ligation Buffer supplemented with 5L of Proximity ligation enzyme and incubated for 4h. After reversing crosslinks, the free DNA was purified with SPRI beads and Hi-C junctions were bound to streptavidin beads and washed to remove unbound DNA. Washed beads were used to prepare paired-end deep sequencing libraries using ProxiMeta Library preparation reagents. Paired-end sequencing (2151bp reads) was carried out on the Illumina HiSeq4K platform. The minimum and average sample depth was 2.3 and 24.5Gb respectively.

Sequencing costs can vary substantially across sequencing centers and countries. Here we provide an estimate based on costs at the Genome Institute of Singapore in November 2021. Based on prices for library preparation kits as described in this manuscript, we estimate that Illumina library preparation costs ~US$50/sample and an Illumina HiSeq sequencing lane costs ~US$1000 with approximate throughput of >350 million paired-end reads (2151bp; >100Gbp). Considering that the average Illumina sequencing depth per sample in this study is ~10Gb, 10 samples can be multiplexed in a single lane, leading to the overall cost per sample of ~US$150. For ONT sequencing, we estimate that with an approximate flow-cell price of US$500 producing ~30Gbp of sequencing data, 5 samples can be multiplexed to obtain the average throughput in this study (~6Gbp). With ONT multiplexed library preparation costs of ~US$50/sample, we estimate that overall ONT costs are also ~US$150/sample. Metagenomic assembly of Illumina and Hybrid datasets with MEGAHIT and OPERA-MS, respectively, typically took less than 3h on an AWS C5 instance with 8 CPUs. Using as reference an AWS C5 instance price of 30 cents an hour for 8 CPUs, this translated to a computational cost of

Illumina and ONT read statistics were generated with Fastq-Scan (v0.4.1, https://github.com/rpetit3/fastq-scan) and NanoStat53 (v1.4.0), respectively. To assess taxonomic concordance, Illumina and ONT reads were classified with Kraken254 (v2.1.1, UHGG database13) and relative abundances were estimated with Bracken55 (v2.6.1) at the species level (option -l R7) to compute Pearson correlation coefficients per sample.

Illumina reads were assembled using MEGAHIT8 (v1.04, default parameters) and hybrid metagenomic assemblies were generated with Illumina and ONT data using OPERA-MS25 (v0.9.0, --polish). Contigs were binned with MetaBAT210 (v2.12.1, default parameters). Hi-C binning was provided by Phase Genomics using its internal pipeline with MetaBAT results for hybrid assemblies as a starting point. Assembly bins were evaluated based on MIMAG standards28, with contamination, completeness and N50 values determined with CheckM56 (v1.04), and non-coding RNA annotations from barrnap (https://github.com/tseemann/barrnap) (v0.9) and tRNAscan-SE57 (v2.0.5, default parameters). Assembly bins with contamination <10% and completeness >50% were designated as medium quality MAGs, those with contamination <5% and completeness >90% as near complete MAGs, and additionally near complete MAGs with complete 5S, 16S, and 23S rRNA genes and at least 18 unique tRNA genes were classified as high quality MAGs. All other bins were classified as low quality and were removed from further analyses. In total, 4497 medium quality, near complete and high quality MAGs were designated as being part of the SPMP database. Hybrid and short-reads assembly based MAGs were further assessed for chimerism with GUNC58 (v1.0.4, detailed output). Coding sequence lengths obtained from Prodigal59 (v2.6.3) calls were compared between the two datasets to assess the potential impact of long read indel errors on gene annotation. Concordant with prior work showing that hybrid metagenomic assemblies can have high base-pair accuracy25, we also noted that SPMP MAGs independently assembled from distinct individual gut metagenomes could exhibit high average nucleotide identity (>99.99%, consistent with Q40 quality).

Representative MAGs for SLCs were used to create a custom Kraken60 (v2.1.1) database (https://github.com/DerrickWood/kraken2/wiki/Manual#custom-databases) and relative abundances for SLCs were estimated for each sample using Bracken55 (v2.6.0, default parameters). Rarefaction analysis for estimating overall species diversity was done using the R package iNext61 (v2.1.7, q=0, datatype=incidence_raw and endpoint=300), based on converting SLC relative abundance values from Bracken into presence-absence values at a threshold of 0.05%.

Genus-level abundances for each sample were provided as input for R package MaasLin236 (v1.4.0) along with sample metadata (age, sex and ethnicity), and significant associations were determined by combining 3 MaasLin2 runs with a compound Poisson linear model.

Metagenomic reads were mapped (--secondary=no) against reference databases indexed with minimap262 (v2.24-r1122, -I 24G; SPMP strain-level genomes and UHGG species-level representatives). Alignments were filtered at the strain-level with bamtools (v2.5.2, -tag NM:<2 -length >99) and unique reads were extracted based on samtools (v1.15.1) view results.

To further evaluate the utility of SPMP genomes relative to the UHGG database for read mapping at the strain-level, we created databases with similar number of strains from both collections. Reference indexing and mapping were done in a similar fashion as described before. Alignments were filtered with pysam (v0.19.1) (read coverage 90%, identity 99%), and reads were classified at the species-level with Kraken (v2.1.1, RefSeq bacteria database). Specifically, we identified 21 species with many strain genomes in UHGG or SPMP (20) and having enough reads (>10 coverage) in at least 3 samples in an independent study of Singaporean gut metagenomes35. Illumina reads were mapped (minimap2, default parameters) independently to strain genomes for each species. Kraken2 classification (standard database) was used to assess if mapped reads came from the right species, and to calculate precision, sensitivity and F1 scores. We noted that median F1 scores were better using SPMP compared to UHGG for 17 out of 21 species. Overall, SPMP provided significantly better mapping performance (F1 score) relative to UHGG for 12 species (Wilcoxon p<0.05). The converse, i.e., significant improvements with UHGG relative to SPMP, were not observed for any species. Improvements in F1 scores were driven by better sensitivity in SPMP vs UHGG for abundant gut bacterial species such Prevotella copri and Alistipes onderdonkii. While median precision scores using SPMP and UHGG were similar (0.98 vs 0.99 for P. copri; 0.98 vs 0.97 for A. onderdonkii), sensitivity was notably higher in SPMP vs UHGG (0.96 vs 0.90 for P. copri; 0.99 vs 0.90 for A. onderdonkii).

The SPMP database was compared to the GTDB database2 (release 95) using GTDBtks63 (v1.4.1) ani_rep command with default arguments, which leverages Mash64 (v2.3) to provide pairwise genome-wide similarity values between all query MAGs and GTDB sequences. Only pairs with Mash distance 0.05 were retained and used to define the best match for each SPMP MAG based on minimum Mash distance. GTDB matches were classified based on their metadata as being uncultivated (derived from environmental sample or derived from metagenome) or based on isolate strains. Both N50 values and MIMAG classifications were extracted from GTDB metadata. MAGs were placed into a phylogenetic tree using GTDB_TK (v1.4.1) with classify_wf (default options), based on pplacer_taxonomy values. To assess novelty in light of the latest human gut metagenome database, we further compared our MAGs to the 5414 representative genomes from the Human Reference Gut Microbiome catalog (HRGM)22 with a similar Mash analysis (Supplementary Data6).

MAGs were clustered at the species (95%) and strain-level (99%) based on average nucleotide identity estimates (ANI; using Mash with sketch size of 10k and k-mer size of 21bp) with agglomerative clustering (sklearn v0.23.2, AgglomerativeClustering function, options: linkage=single, n_clusters=None, compute_full_tree=True, affinity=precomputed). For each cluster, representative MAGs were defined using the highest eigen centrality value based on a weighted network graph produced by networkx (v2.5; eigenvector_centrality function). Strain-level clustering was done jointly with all species-level matches from the UHGG database (v1.0, ANI threshold of 95%). Phylogenetic analysis at the strain-level was conducted using the biopython Phylo package65, based on pairwise distances generated with FastANI66 (v1.32). Phylogenetic trees were visualized using FigTree (tree.bio.ed.ac.uk/software/figtree).

SLCs were assigned putative species name and types based on comparisons with multiple databases, including GTDB, Pasolli et al.67 (SGB) and Almeida et al.13 (UHGG). SLCs types were defined as, (i) isolate: if GTDB match to an isolate was found (Mash distance 0.05), (ii) uncultivated: if a match to any database was found, but no isolates, (iii) novel: if no matches were found. SLCs were assigned putative species names based on a majority rule for MAGs in the cluster, with preference for GTDB ids (Supplementary Fig.9).

Biosynthetic gene clusters (BGCs) in the SPMP database were identified using antiSMASH68 (v5.1.2, --genefinding-tool prodigal-m --cb-general --cb-knownclusters --cb-subclusters --asf --pfam2go --smcog-trees) and DeepBGC38 (v0.1.18, prodigal-meta-mode). BGCs with only one identified gene and with length <2kbp were removed for both sets of results. For antiSMASH this provided a set of 3,909 BGCs. DeepBGC results which overlapped with antiSMASH were removed if the genomic coordinates of both BGCs overlapped by 30% in either direction. DeepBGC candidates were further filtered for (i) being categorized with a known product class and (ii) containing at least one known biosynthetic pfam or TIGRFAM protein domain as defined by Cimermancic et al.69, providing an additional set of 23,175 BGCs.

All 27,084 BGCs (3909 from antiSMASH + 23,175 from DeepBGC) were first categorized into different product classes: ribosomally synthesized and post-translationally modified peptides (RiPPs), nonribosomal peptide synthetases (NRPs), polyketide synthases (PKS), saccharides and others based on the labels reported by each algorithm. We further unified the antiSMASH and DeepBGC product class labels to integrate both datasets (Supplementary Table1). A fraction of mined BGCs were labeled as hybrids because antiSMASH or DeepBGC associated them with two different product classes e.g., bacteriocin;T1PKS. The BGCs in each product class were grouped into gene cluster families (GCFs) by sequence similarity using BiG-SCAPE39 (v1.01, --include_singletons --mix --no_classify --cutoffs 0.3). A total of 16,055 GCFs were defined by this approach and for each GCF we took the smallest BGC member as a representative of the family. Gene cluster diagrams of BGCs were created using Clinker70.

BGCs in SPMP were classified as novel via a two-step approach. Firstly, BGC sequences were required to have <80% similarity to any existing sequence in the antiSMASH and MIBiG 2.071 databases using the clusterblast results from antiSMASH. Secondly, BGC annotations were compared to antiSMASH annotations from a comprehensive gut microbial genome collection (HRGM) using the standalone clusterblast software72 (v 1.1.0), to identify SPMP matches based on a 80% similarity threshold, similar to the approach described in Gallagher et al73.

Besides bacteriocins, BGC mining in the SPMP database also identified other classes of ribosomally synthesized and post-translationally modified peptides (RiPPs) such as lanthipeptides and lassopeptides (Supplementary Fig.15A), which can also possess antimicrobial properties. Antimicrobial activities of putative peptides encoded by novel RiPP BGCs in SPMP were predicted using an ensemble voting approach (Supplementary Fig.15B) with four different AMP prediction models: AMPscanner74 (v2, convolutional neural network), AmpGram75 (random forest model), AMPDiscover76 (based on quantitative sequence activity models) and ABPDiscover (https://biocom-ampdiscover.cicese.mx/). Peptides predicted by antiSMASH in these RiPP BGCs were translated and all amino acid sequences with a length greater than 10 but lesser than 200 were used as inputs into these four models. Peptides were classified as AMPs if they received votes from both AMPscanner and AmpGram, and at least one vote from either AMPDiscover or ABPDiscover, and if the corresponding RiPP BGCs contained a transporter protein. The performance of this ensemble approach was evaluated using 78 known AMP sequences and 78 scrambled non-AMP sequences taken from the AmpGram benchmark dataset75. For our evaluation dataset, we identified and removed all sequences that were found in the training sets of AMPscanner, AmpGram, AMPDiscover and ABPDiscover using seqkit77 (v0.11.0) and samtools faidx (v1.9). The percentage hydrophobicity and overall charge of selected peptide sequences was determined using the AMP calculator in the AMP database 3 (APD3; https://aps.unmc.edu/prediction).

Out of 107 RiPP BGCs that were not bacteriocins, 54 of them were predicted to also be AMPs. One of these was found to be a lanthipeptide (GCF459) in Dorea longicatena B (Supplementary Fig.15C), with no significant blastp matches to the NCBI nr database. This peptide sequence has a 32% hydrophobic amino acid composition and a net positive charge of +5, which could favor its insertion into the cell walls and membranes of its targets. Another novel AMP is a lassopeptide (GCF26) found in a Ruminococcus species (Supplementary Fig.15D), with similarly high proportion of hydrophobic amino acids (35%) and a slight net positive charge.

To associate BGC presence/absence patterns with microbial community structure, correlation analysis (Fastspar78 v1.0.0, parameters: --iterations 100 --exclude_iterations 20, p-values from 1000 bootstrap replicates and permutation testing) was done based on SLC abundance profiles across samples (species with medium abundance 0.1% filtered out). Correlations in the network were kept if they had an associated p-value <0.05.

No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Further information on research design is available in theNature Research Reporting Summary linked to this article.

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Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians - Nature.com

How much does an electric vehicles battery performance change in hot weather? How about cold?

If someone drives aggressively in an EV, how does that affect the battery life?

How much do variations in battery materials make a difference in how an EV performs in various conditions?

Researchers and manufacturers have partial answers to these questions based on the data they have collected. But they would know much more if they shared their data in formats they all could understand.

ICN provides award-winning climate coverage free of charge and advertising. We rely on donations from readers like you to keep going.

This is the premise behind the Battery Data Genome, a new initiative led by Argonne National Laboratory in Illinois and Idaho National Laboratory, among others. The name is a reference to the Human Genome Project, a monumental data-sharing project launched in 1990 that contributed to innovations in medical science.

Its going to take a lot of data, data from a lot of sources, said George Crabtree, a distinguished fellow at Argonne and director of the Department of Energys Joint Center for Energy Storage Research.

Crabtree is one of more than two dozen co-authors of a paper published this month in the journal Joule announcing the project. Regular readers will recognize him as someone I often ask to help translate battery science into plain language.

The Battery Data Genome will collect information from every part of the battery life cycle, including basic data like how batteries respond to different types of charging and discharging, and additional variables like the effects of temperature, driving speed and differences in the materials within the batteries.

The participants include national labs, like Argonne and Idaho, and anyone else who wants to join, which could include universities, automakers and other businesses. The partners can choose how much they want to share.

I think one of the things that everyone realizes is that some will be reluctant to join, because, you know, it compromises their secrets, trade secrets, and thats OK, Crabtree said. Its kind of an open decision for anyone who wishes to participate.

The project is aiming to create a common set of standards for how battery data is formatted, so everyone is speaking the equivalent of the same language.

Then, when there are vast amounts of data in one place, the organizers are hoping that researchers and companies can use artificial intelligence and other sophisticated methods of analysis to unlock ways to make batteries that are more effective.

Sue Babinec, an Argonne battery scientist, said in an email that the announcement of the project follows more than a year of meetings and conversations among researchers about how to standardize their data for better sharing. She was the lead writer of the paper, along with Eric Dufek, a manager at the Idaho lab.

The authors work is an attempt to counteract what the paper says is the current fragmented ecosystem in the ability of researchers to build on each others progress, which is holding back the potential for a renaissance in battery data science.

The paper notes that there are already several data-sharing initiatives in battery science, including the Battery Data Toolkit maintained by Argonne. The new project is building on what the others have done.

Consumers, businesses and the research and development community would be the beneficiaries because of research that should make batteries less expensive, more functional and more durable. This would apply to batteries used in EVs along with stationary battery storage and other applications.

Crabtree sees the potential for the insurance industry to use some of the data to get a better idea of how to insure EVs. Also, consumers may be able to allow their driving habits to be monitored, and drivers who are putting less stress on their batteries may be able to qualify for lower rates.

The most exciting thing, he said, is the idea that sharing data on a large scale can yield insights that are beyond even what researchers know to be looking for, insights that otherwise would not be available.

Other stories about the energy transition to take note of this week:

Honda and LG to Spend Billions to Build a Battery Plant in Ohio: Honda announced that it has picked a site southwest of Columbus, Ohio, to build a $3.5 billion plant to build batteries for electric vehicles. The plant, part of a joint venture with LG, will be in Fayette County, Ohio, which is just outside of the Columbus metro area, as Mark Williams reports for The Columbus Dispatch. The new plant will employ 2,200 workers, making this an economic development coup for the state. Honda also said it is spending $700 million to retool three existing plants in Ohio to prepare them to make electric vehicles. We now face a once-in-a-100-years change from the internal combustion engine to electrification, said Bob Nelson, executive vice president of American Honda Motor Co. Once again this requires a bold vision for the future. Ive been writing about opposition to solar power in Pickaway County, Ohio. This new plant will be about 30 miles from Williamsport, the village where many residents are opposed to installing solar on farmland. It will be interesting to see how this rural region responds to this wave of development, which is likely to lead to pressure to build housing subdivisions on land that is now used for farming.

A Close Look at the Grassroots Clean Energy Revolution: Canary Media has a series of stories this week about communities taking charge of clean energy development after utilities and the government failed to do so. The availability of cheap solar, batteries and other tools gives communities new options to cleanly power themselves, and neighborhoods across the country are availing themselves of this opportunity, writes Julian Spector in an introduction to the series. Among the stories, Jeff St. John writes about how a new generation of Indigenous leaders are building businesses and serving their communities with clean energy.

The Climate Lawand Its BillionsAre Changing Everything: The new climate law is influencing everything from how consumers buy cars and how green groups are organizing to which policy experts are suddenly in high demand. And this is just two months after President Joe Biden signed the bill, as Robin Bravender reports for E&E News. The country hasnt embarked on this level of industrial transformation since the New Deal, said Sam Ricketts, co-founder of Evergreen Action and a senior fellow at the Center for American Progress. This is going to be a thing we are all going to be figuring out together.

GE Begins Restructuring Its Onshore Wind Business to Adjust for Market Realities: GE Renewable Energy has confirmed that it is restructuring its onshore wind operations following media reports that the company was laying off workers. GE did not confirm the size or the timing of the cuts, as Emma Penrod reports for Utility Dive. GE Renewable Energys wind business has struggled to deal with a decrease in orders due to competition from other manufacturers, rising costs and other challenges. We are taking steps to streamline and size our onshore wind business for market realities to position us for future success, a company spokesperson said to Utility Dive.

GM to Buy a Stake in Australian Mining Company to Gain New Sources of Nickel and Cobalt for EVs: General Motors has said it will invest up to $69 million in Queensland Pacific Metals of Australia. The move will give GM access to cobalt and nickel for making batteries for electric vehicles, as David Shepardson reports for Reuters. The investment will help GM to maximize the incentives available to consumers under new tax credits, which are limited to vehicles with batteries whose materials were produced in the United States or in countries that have free trade agreements with the United States.

Inside Clean Energy is ICNs weekly bulletin of news and analysis about the energy transition. Send news tips and questions to dan.gearino@insideclimatenews.org.

Dan Gearino covers the midwestern United States, part of ICNs National Environment Reporting Network. His coverage deals with the business side of the clean-energy transition and he writes ICNs Inside Clean Energy newsletter. He came to ICN in 2018 after a nine-year tenure at The Columbus Dispatch, where he covered the business of energy. Before that, he covered politics and business in Iowa and in New Hampshire. He grew up in Warren County, Iowa, just south of Des Moines, and lives in Columbus, Ohio.

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The mammal ancestor had 19 autosomal chromosomes and 2 sex chromosomes.

From a platypus to a blue whale, all living mammals today are descended from a common ancestor that existed some 180 million years ago. Although we dont know a lot about this animal, a global team of experts has recently computationally reconstructed the organization of its genome. The findings were recently published in the journal Proceedings of the National Academy of Sciences.

Our results have important implications for understanding the evolution of mammals and for conservation efforts, said Harris Lewin, distinguished professor of evolution and ecology at the University of California, Davis, and senior author on the paper.

The researchers used high-quality genome sequences from 32 living species, spanning 23 of the 26 known mammalian orders. Humans and chimpanzees were among these species, as were wombats and rabbits, manatees, domestic cattle, rhinos, bats, and pangolins. The chicken and Chinese alligator genomes were also used as comparison groups in the analysis. Some of these genomes are being produced as part of the Earth BioGenome Project and other large-scale biodiversity genome sequencing initiatives. Lewin is the chair of the Earth BioGenome Projects Working Group.

According to Joana Damas, the first author of the study and a postdoctoral researcher at the UC Davis Genome Center, the mammal ancestor had 19 autosomal chromosomes, which control the inheritance of an organisms characteristics other than those controlled by sex-linked chromosomes (these are paired in most cells, making 38 in total), plus two sex chromosomes. The researchers identified 1,215 blocks of genes that appear on the same chromosome in the same order across all 32 genomes. Damas said that these building blocks of all mammal genomes include genes that are essential for the development of a normal embryo.

The researchers found nine whole chromosomes or chromosome fragments in the mammal ancestor whose order of genes is the same in modern birds chromosomes.

This remarkable finding shows the evolutionary stability of the order and orientation of genes on chromosomes over an extended evolutionary timeframe of more than 320 million years, Lewin said.

In contrast, regions between these conserved blocks contained more repetitive sequences and were more prone to breakages, rearrangements, and sequence duplications, which are major drivers of genome evolution.

Ancestral genome reconstructions are critical to interpreting where and why selective pressures vary across genomes. This study establishes a clear relationship between chromatin architecture, gene regulation, and linkage conservation, said Professor William Murphy, Texas A&M University, who was not an author of the paper. This provides the foundation for assessing the role of natural selection in chromosome evolution across the mammalian tree of life.

The researchers were able to follow the ancestral chromosomes forward in time from the common ancestor. They found that the rate of chromosome rearrangement differed between mammal lineages. For example, in the ruminant lineage (leading to modern cattle, sheep, and deer) there was an acceleration in rearrangement 66 million years ago when an asteroid impact killed off the dinosaurs and led to the rise of mammals.

The results will help to understand the genetics behind adaptations that have allowed mammals to flourish on a changing planet over the last 180 million years, the authors said.

Reference: Evolution of the ancestral mammalian karyotype and syntenic regions by Joana Damas, Marco Corbo, Jaebum Kim, Jason Turner-Maier, Marta Farr, Denis M. Larkin, Oliver A. Ryder, Cynthia Steiner, Marlys L. Houck, Shaune Hall, Lily Shiue, Stephen Thomas, Thomas Swale, Mark Daly, Jonas Korlach, Marcela Uliano-Silva, Camila J. Mazzoni, Bruce W. Birren, Diane P. Genereux, Jeremy Johnson, Kerstin Lindblad-Toh, Elinor K. Karlsson, Martin T. Nweeia, Rebecca N. Johnson, Zoonomia Consortium and Harris A. Lewin, 26 September 2022, Proceedings of the National Academy of Sciences.DOI: 10.1073/pnas.2209139119

The study was funded by the National Institutes of Health and the U.S. Department of Agriculture.

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Scientists Reconstruct the Genome of the 180-Million-Year-Old Common Ancestor of All Mammals - SciTechDaily

Dereplication of Neonectria discophora SNB-CN63 metabolomes and Penicillium sclerotiorum SNB-CN111

The ICSN strain collection includes 130 strains of termite mutualistic microorganisms from French Guiana. Each strain was cultivated on solid PDA medium and then extracted by ethyl acetate. The specialized metabolomes of each extract were explored by reverse-phase liquid chromatography coupled with positive electrospray ionization tandem mass spectrometry (RPLC-ESI(+)-MS/MS). The MS/MS data were organized and visualized as a molecular network based on fragmentation spectra homology related to structural homology27 with MetGem software38 based on t-SNE visualization. We analyzed the production of specialized metabolites with high structural specificity in which nodes were clustered (Fig.1, top). We identified ilicicolins produced by Neonectria discophora SNB-CN63 (blue cluster in Fig.1) and azaphilones produced by Penicilliumsclerotiorum SNB-CN111 (red cluster in Fig.1).

Molecular network obtained for specific specialized metabolomes of P. sclerotiorum and N. discophora among 130 crude extracts of termite-associated microorganisms (top). MetGem software (https://metgem.github.io/). Chlorinated metabolites have been annotated by comparison of their MS/MS spectra with databases and are depicted with their typical isotopic pattern related to 37Cl (bottom).

In these two clusters, we observed a specific isotopic pattern at the MS level of chlorinated metabolites via the natural abundance of 37Cl isotopes (24.23%) versus 35Cl (75.77%)39. Thereafter, in the blue cluster, 12 molecules were annotated as ilicicolins by comparison with public or internal databases from previous studies (Table S1, Figure S1)17,18. Eight of these molecules bear a chlorine atom, such as LL-Z1272 or ilicicolinal (1), ilicicolinic acid A (2) and ilicicolinic acid C (3) (Fig.1). In the red cluster, twenty-three azaphilones were annotated using MS/MS spectral comparisons from public or in-house databases (Table S2, Figure S2)20,40,41. Among them, 14 are chlorinated, such as sclerotiorin (4), sclerotioramine (5) or 5-chloroisorotiorin (6), which were previously isolated in our group20,42,43,44.

Ilicicolins are reported as intermediates of halogenated metabolites named ascofuranone and ascochlorin, which are chlorinated by a FAD-dependent halogenase (AscD)45. In the literature, 28 ilicicolin or acid ilicicolinic scaffolds isolated from natural products have been reported, among which 22 are chlorinated46.

In a review article published in 2021, Pavesi et al. reported 676 azaphilones, among which 152 contain a chlorine atom47. These chlorinated metabolites are included in just four azaphilone subfamilies, i.e., chaetoviridins, falconensins, sclerotiorins and luteusins. Among these scaffolds, only the gene cluster involved in chaetoviridin biosynthesis has been elucidated. In that particular case, the enzyme involved in chlorine addition is also a FAD-dependent halogenase (CazI)48.

Hence, we hypothesized that FAD-dependent halogenases catalyze the halogenation of ilicicolins and azaphilones produced by N.discophora SNB-CN63 and P.sclerotiorum SNB-CN111, respectively45,48. To look for these enzymes, we sequenced these two fungal genomes using a combined long- and short-read sequencing approach followed by a hybrid assembly.

The N. discophora genome (ENA accession number: GCA_911649645) was obtained in 26 contigs covering 41.6 Mbp and characterized by a GC content of 54.2%. The completeness of the genome was established at 99.3% using the BUSCO score, and 12,267 genes were predicted (Tables S3, S4)49. The contiguity of the genome assembly is characterized by an N50 length of 4.04 Mbp and an L50 of five. We used the antiSMASH pipeline to predict the existence of a biosynthetic gene cluster31. Two biosynthetic gene clusters were predicted to include a halogenase as a tailoring enzyme. Sequence comparisons using halogenases from the SwissProt and UniProt databases confirmed that no other putative halogenase was present in the N. discophora SNB-CN63 genome. Four genes, a non-reducing polyketide synthase, a prenyltransferase, a non-canonical non-ribosomal peptide synthetase and a halogenase, from candidate cluster Ndi_Ili are similar to the ascofuranone biosynthetic gene cluster (59 to 79% similarity, Table S5). Only the halogenase from candidate cluster Ndi_WSC72 shares 70% similarity with ascD, the ascofuranone halogenase (Table S6). A synteny analysis visualized with clinker50 further confirm the stronger similarity between Ndi_Ili and the ascofuranone biosynthetic gene cluster (Figure S3). Thus, we concluded that Ndi_Ili was the best candidate cluster for ilicicolins biosynthesis45. This ilicicolin biosynthesis cluster is composed of 10 genes named Ndi_Ili_A-J (Fig.2, Table S5).

Annotated biosynthetic gene cluster of N. discophora SNB-CN63 and related biosynthetic pathways. NR-PKS: nonreducing polyketide synthase, NRPS: nonribosomal peptide synthase. KS: keto-synthase, AT: acyltransferase, PT: product template, ACP: acyl carrier protein, TE: thioesterase. Chemdraw software.

The nonreducing polyketide synthase Ndi_Ili_A is composed of five domains, keto-synthase, acyltransferase, product template, acyl carrier protein, and thioesterase, and it is predicted to catalyze orsellinic acid formation (7) since it shares 56% identity and 70% similarity with AscC, as described in Acremonium egyptiacum45. The prenyltransferase Ndis_Ili_B produces grifolic acid (8) from orsellinic acid by the addition of a farnesyl group (observed at [M+H]+, m/z 373.2373, err. 0.0ppm, C23H32O4), and then a noncanonical nonribosomal peptide synthase Ndi_Ili_C reduces the carboxylic acid function to form ilicicolin B, also named LL-Z 1272 (9) ([M+H]+, m/z 357.2422, err. 0.6ppm, C23H32O3). Finally, the FAD-dependent halogenase Ndis_Ili_D adds a chlorine atom to the orsellinic scaffold to form Compounds 1 and/or 2 ([M+H]+, m/z 391.2028, err. 1.7ppm, C23H31ClO3 and [M+H]+, m/z 407.1979, err. 1.1ppm, C23H31ClO4, respectively), indicating some flexibility of halogenase regarding its substrates (Table S7). It is likely that the formation of other compounds previously described in the literature from this strain45, such as ilicicolinals and ilicicolinic acids, involves the action of monooxygenase, epoxidase and terpene cyclase acting on the prenyl chain. These enzymes are probably located outside the Ndis_Ili gene cluster, as determined by Araki et al. for ascochlorin and ascofuranone45.

The genome of P. sclerotiorum SNB-CN111 (ENA accession number: GCA_911649655) was obtained in 10 contigs covering a total size of 34.7 Mbp and a GC content of 48.3%. The completeness of the genome was established at 98.3% using the BUSCO score, and 12,582 genes were predicted (Tables S3, S4)49. The contiguity of the genome assembly is characterized by an N50 length of 4.34 Mbp and an L50 of four. We used the antiSMASH pipeline to annotate a biosynthetic gene cluster. We predicted 15 clusters with polyketide synthase as core enzymes, with three of them containing two polyketide synthases. Only one of these three clusters with two polyketide synthases included a halogenase31. This putative azaphilone biosynthesis gene cluster is composed of 13 genes named Psc_Aza_A-M (Fig.3, Tables S8, S9). The Psc_Aza_A protein was identified using a Pfam search as a highly reducing polyketide synthase composed of seven modules: -ketoacyl synthase, acyltransferase, dehydratase, methyltransferase, enoylreductase, keto-reductase and acyl carrier protein (phosphopantetheine attachment site). This sequence is typical of highly reducing polyketide synthases such as ATEG_07659 (65% identity, 77% similarity) involved in the biosynthesis of azaphilones such as asperfuranone51. The Psc_Aza_B gene is predicted to encode a nonreducing polyketide synthase composed of five domains: -ketoacyl synthase, acyltransferase, acyl carrier protein (phosphopantetheine attachment site), methyltransferase and a terminal domain. The enzyme CazM, a nonreducing polyketide synthase involved in chaetoviridin synthesis, is the closest homolog (67% identity, 78% similarity) to Psc_Aza_B52. Other azaphilone biosynthetic pathways involving both highly reducing and nonreducing polyketide synthases are described in the literature47,53. A synteny cluster comparison using clinker50 revealed that the polyketide synthase pair involved in other azaphilone biosynthetic pathways (i.e., chaetoviridin, asperfuranone, azanigerone, mitorubrinol and ankaflavin) is conserved and homologous to Psc_Aza_A and Psc_AzaB (Figure S4). These polyketide pairs can operate sequentially, with a highly reducing polyketide synthase producing the first precursor, which is then transferred to nonreducing polyketide synthase and extended (asperfuranone)54. They may also operate in a convergent manner with both enzymes being responsible for the biosynthesis of polyketides that are assembled together at later steps (azanigerone)56 or in a hybrid manner with both sequential and convergent modes (chaetoviridin)55. Finally, Psc_Aza_C, a FAD-dependent monooxygenase, is found in the azaphilone biosynthetic gene cluster47. FAD-dependent monooxygenases play a key role in azaphilone synthesis as they are required for the cyclization of the pyran ring. The high sequence similarity of the Psc_Aza_A/Psc_Aza_B polyketide synthases with the CasF/CazM and ATEG_07659/ATEG_07661 couples suggests that the azaphilone biosynthetic pathway is similar to that of asperfuranone or chaetovirin (Figure S4). Moreover, Psc_Aza_A/B/C/D/E/G/H/L are similar to other proteins involved in other azaphilone biosynthetic pathways (Figure S4), strengthening our annotation identification of Psc_Aza as a putative azaphilone biosynthetic pathway responsible for the production of sclerotiorin.

Annotated biosynthetic gene cluster of P. sclerotiorum SNB-CN111 and related biosynthetic pathways. NR-PKS: nonreducing polyketide synthase, HR-PKS: highly reducing polyketide synthase. KS: keto-synthase, AT: acyltransferase, DH: dehydratase,MT: methyltransferase, ER: enoylreductase, KR: ketoreductase, ACP: acyl carrier protein, TD: terminal domaine Chemdraw software.

Psc_Aza_A catalyzes the elongation of the 4,6-dimethyl-2,4-octadienal unit, and cyclization is then performed by Psc_Aza_B. The monooxygenase Psc_Aza_C then catalyzes the cyclization of the pyran ring and the formation of the azaphilone scaffold (Fig.3). This hypothesis about the initiation of the azaphilone biosynthetic pathway is strengthened by the detection in the molecular network of an ion of m/z 317.1747 (err. 0.1ppm) corresponding to the molecular formula C19H24O4, whose fragmentation spectrum agrees with the structure of metabolite 10 resulting from Psc_Aza_B catalysis (Figure S5). The [M+H]+ ion of compound 11 is observed at m/z 333.1699 (err. 0.8ppm), which corresponds to the molecular formula C19H24O5 expected for the biosynthetic intermediate resulting from the biotransformation of metabolite 10 by Psc_Aza_C. The fragmentation spectrum of this ion at m/z 333.1699 confirms the proposed structure of intermediate compound 11 (Figure S6). Metabolite 12, originating from the spontaneous conversion of metabolite 11, was detected as an [M+H]+ ion at m/z 315.1594 (err. 1.0ppm, C19H22O4), leading to a typical fragmentation spectrum from the sclerotiorin scaffold with a fragment at m/z 147.0457 (err. 7.9ppm) and has not been observed until now for compounds 10 and 11 (Figure S7)20. Molecule 12 is also described as an azaphilone intermediate involved in the asperfuranone biosynthesis pathway56.

Three biosynthetic pathways are possible with this azaphilone scaffold (12). The first pathway leads to the production of sclerotiorin (4). It is mediated by the action of an acyl transferase Psc_Aza_D to form compound 13 ([M+H]+, m/z 357.1705, err. 2.4ppm, C21H24O5). Then, the FAD-dependent halogenase Psc_Aza_H leads to the production of molecule 4 ([M+H]+, m/z 391.1309, err. 0.6ppm, C21H23ClO5). The second biosynthetic pathway leads to the formation of isochromophilone I (17) and is also initiated by the action of an acyltransferase, probably Psc_Aza_D, which adds an acetoacetate to form compound 14 (not detected) that is spontaneously converted by Knoevenagel condensation into compound 15 ([M+H]+, m/z 381.1691, err. 1.5ppm, C23H24O5)42. The angular lactone is then hydrogenated by the action of Psc_Aza_F or G to form compound 16 ([M+H]+, m/z 383.1859, err. 1.6ppm, C23H26O5), which is then chlorinated by the action of Psc_Aza_H. The third biosynthetic pathway leads to the formation of compound 20. The formation of molecule 20 requires the action of an oxidoreductase to reduce C-6 ketone and form a hydroxyl, a dehydrogenase (to hydrogenate the C-1/C-8a bond), an acyltransferase and a halogenase. However, without the detection of intermediates between compounds 12 and 19, it is not possible to define which enzymes are involved and in which order. The enzymes Psc_Aza_D, E, and F/G could be involved in the biosynthesis of metabolites 12 to 19 because of their Pfam domains and their inclusion in the azaphilone biosynthetic gene cluster. Finally, Psc_Aza_H catalyzes the chlorination of molecule 19 ([M+H]+, m/z 361.2021, err. 3.2ppm, C21H28O5) to form compound 20 ([M+H]+, m/z 395.1626, err. 1.6ppm, C21H27ClO5). Notably, compound 21, the chlorinated analog of intermediate 12 (m/z 349.1205, err. 1.1ppm, C19H21ClO4) was also detected (Figure S8), suggesting that the FAD-dependent halogenase Psc_Aza_H may catalyze chlorination as soon as the azaphilone scaffold is formed.

Thus, we completed the annotation of the biosynthetic gene cluster of sclerotiorin (4) and isochromophilone I (17). Both compounds originated from an intermediate with a minimal azaphilone scaffold with a 3,5-dimethyl-1,3-heptadienyl chain (molecule 12) that is typical of sclerotiorin and its analogs (Fig.3). The gene coding for a halogenase, Psc_Aza_H, was identified, as well as numerous chlorinated azaphilone intermediates or analogs (Tables S2, S8). The FAD-dependent halogenase may be involved as early as the formation of the azaphilone scaffold since intermediate 21 is the chlorinated analog of compound 12.

In summary, we identified two clusters of biosynthetic genes that may be responsible for the production of two chlorinated polyketide families: ilicicolins and azaphilones. For each cluster, we annotated a FAD-dependent halogenase. We then applied the OSMAC method to generate structural diversity and to confirm the ability of halogenases in the biosynthetic pathways to introduce various halogens (Cl, Br and I).

Several studies have highlighted the ability of FAD-dependent halogenases to introduce different halogens, such as Cl, Br and I, into various chemical scaffolds57,58,59. Therefore, we sought to generate new compounds taking into account this biosynthetic possibility from our two sequenced strains: N. discophora SNB-CN63 and P. sclerotiorum SNB-CN111. For this purpose, we cultivated strains on PDA media supplemented with 10gL1 NaCl, KBr or KI without affecting microorganism growth. We further analyzed crude extracts by RPLC-ESI(+)-MS/MS to highlight and annotate the major halogenated biosynthesized analogs (Fig.4a).

Generation of halogenated azaphilones and ilicicolins by the OSMAC method. (a) Extracted ion chromatograms of halogenated azaphilones from scaffold A (H, Cl, Br and I) with their isotopic patterns. (b) Identified halogenations from Ndi_Ili_D on ilicicolin scaffolds A and B. (c) Identified halogenations from Psc_Aza_H on azaphilone scaffolds B, C, D and E.

We searched the m/z values of the protonated species corresponding to the halogenated (Cl, Br and I) metabolites in the MS data. We also examined the isotopic profiles and the retention time (RT), which evolves with the sizes of the halogen substituting H, i.e., H

We performed a scale-up culture to confirm our structural annotations and to demonstrate that halogenase promiscuity can be exploited to produce sufficient quantities of new and isolable compounds. We used Czapek medium (Czk) to scale up the culture of P. sclerotiorum because no organic nitrogen is provided in this medium, thereby leading to a reduced number of produced metabolites. As brominated molecules from scaffolds B and E have already been described, we focused on scaffold A60,61,62. Furthermore, nitrogenated azaphilone-like molecules bearing scaffold D were not produced in Czk medium, and bromine molecules related to scaffold C were not abundant enough. Therefore, compound 22, which is related to scaffold A and has incorporated bromine, was produced and isolated. Compound22 was obtained as an orange oil and its molecular formula was determined to be C23H23BrO5 based on the ESI-HRMS experiment ([M+H]+ peak at m/z 459.0798 calcd for C23H23BrO5H+, err. 0.7ppm) (Fig.5). The azaphilone scaffold was identified by 13C NMR of carbon with chemical shifts at C of 153.2, 159.7 and 184.5 (C-1, C-3 and C-6, respectively) and validated by HMBC correlations of H1/C-3, C-4a, and C-8a, H4/C-3, C-5, C-8, C-8a and finally H18/C-6, C-7 and C-8. In addition, the HMBC correlations of H-9 and H-10 with C-3 and H-4 with C-3 allowed the side chain to be connected. The lactone moiety was confirmed by the presence of 4 carbons, including 2 carbonyls observed at C 195.1 and 169.4, one methene (at C 124.8), one methyl group (C 30.9) and by HMBC correlations between H-5 and C-3 and C-4. The 3,5-dimethyl-1,3-heptadienyl unit was established by typical correlations of trans-coupled olefinic protons observed in COSY with correlations between H-9/H-10, H-12/H-13/H-16 and H-13/H-14/H-15. As no proton was observed at the C-5 position on the HSQC spectrum, the bromine atom was positioned there (Figures S15-20). This attribution is in accordance with the previously-reported NMR characterization of compounds with the same scaffolds as 15 and 6 (Tables S10, S11)44,60. Compound 22 was described for the first time and was named 5-bromoisorotiorin (Figure S21).

Structural elucidation of compound 22 and 2D RMN correlations observed: 1H-1H COSY (in bold) and 1H-13C HMBC (arrows).

To date, the isolation and identification of brominated azaphilones has been described in only three publications, and all of them reported the isolation from marine sponge-derived fungi of the Penicillium genus (P. canescens and P. janthinellum)62,63. The authors cultivated these strains with NaBr to obtain these five brominated azaphilones. The new brominated metabolite 22 was also obtained by an OSMAC method using halogenase promiscuity but for the first time from a terrestrial fungus.

In a previous study concerning mutualistic strains isolated from termites, we showed that the PDA extract of SNB-CN111 had antifungal activity against Trychophyton rubrum20. Therefore, we compared the antimicrobial activity of previously isolated azaphilones 5-chloroisorotiorin (15) and sclerotiorin (4) with the newly characterized azaphilone 5-bromoisorotiorin (22) on the same human pathogen, i.e., Tricophyton rubrum. We obtained a minimal inhibitory concentration (MIC) value of 32gmL1 for the azaphilone extract and the three individual compounds. Therefore, halogens on azaphilone scaffolds do not seem to modulate the antimicrobial activity of azaphilone, but the promiscuity of Psc_Aza_H halogenase offers the opportunity to generate undescribed natural compounds.

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Combining OSMAC, metabolomic and genomic methods for the production and annotation of halogenated azaphilones and ilicicolins in termite symbiotic...

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IPM: What is Bionano Genomics mission in advancing precision medicine?

Our mission is to transform the way the world sees the genome. In precision medicine, transformation depends on providing researchers better tools to identify genomic variation that matters in human health and disease. If we want to understand the genomic underpinnings of cancer and genetic disease more fully, we have to look across the variant continuum from single nucleotide variants (SNVs) and small indels to larger structural variants (SVs). While next-gen sequencing (NGS) technologies do an excellent job of interrogating the genome for small variants, they have real limitations when it comes to SVs. In addition, many traditional techniques for detecting SVs, like karyotyping and FISH, have shortcomings. We see a huge opportunity for optical genome mapping (OGM) to address these limitations, and for our software platforms to pull together multiple data types into a single view. With these solutions, were equipping labs with new ways to advance basic, translational, and clinical research.

IPM: How has Bionano Genomics evolved to work toward accomplishing that mission?

We began as a traditional life sciences instrument company, with the development of the instrument and consumables for OGM. However, we quickly identified the need within the market to make sense of multiple data types, such as NGS data. We saw an opportunity to address this need through data visualization, which prompted our acquisition of BioDiscovery. This enabled us to leverage NxClinical software, widely considered to be one of the most powerful tools for visualization, interpretation, and reporting of genomic variants from NGS, microarray, and soon, OGM data.

IPM: How can a better understanding of OGM tools and the structural variations they can identify help advance precision medicine?

A growing body of evidence supports our belief that OGM can play a primary role in detecting and understanding SVs present in various forms of disease.

Many studies, including a recent one published in Leukemia by MD Anderson Cancer Center, have revealed OGMs ability to find more clinically relevant pathogenic variants compared to traditional cytogenetic techniques. This study shows that OGM can have higher resolution, be faster, and reveal more variants than traditional methods, attributes that may play a role in disease management. It also shows that combining OGM with NGS can offer a workflow that reveals SVs and SNVs in a way that the standard combination of tools in use today cannot.

IPM: What future technology developments can we expect from Bionano Genomics, and what does that roadmap look like?

Bionano maintains an active product development pipeline to grow our customer base in new markets. Were currently working on new DNA isolation and labeling protocols, and weve recently announced a collaborative development with Hamilton to provide the worlds first automation solution for Ultra High Molecular Weight extraction used in OGM.

One of our most ambitious development projects includes a new genome-mapping instrument which will provide substantially higher sample throughput than our current instrument, the Saphyr system. Understanding the sample volumes processed by large reference laboratories and CROs, were developing this new instrument with increased OGM throughput capabilities to meet the needs of these users.

A second development project will strengthen our software portfolio with a new version of the NxClinical software. This new software will enable OGM SV visualization alongside other data types from most sequencing and array platforms. These consolidated analysis capabilities will let labs visualize all their existing data sourcesarray, NGS (both short- and long-read sequencing), and soon, OGMall from within one software platform. We believe the cumulative impact this consolidation will have on precision medicine research could be a game-changer.

Overall, were driving toward delivering an end-to-end workflow that begins with data collection from the OGM instruments all the way to fully featured variant visualization, interpretation, and reporting software.

For additional information: http://www.bionanogenomics.com

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Uncovering the Full Variant Continuum with Pioneering Solutions from Bionano - Inside Precision Medicine

Overview of the viromes

In all, 41 wild Qinghai voles were collected from pasture habitats located on the eastern Tibetan Plateau, China (Fig.1). Tissue samples from liver, lung, spleen, small intestine (with content), and feces (large intestinal content) of each vole were disrupted, and viral RNA was extracted. The RNA samples were combined into 20 pools of equal quantities according to sample type (Supplementary Table S1). Overall, 729,234,124 paired-end reads with an average of 150bp in length were obtained from 20 libraries, yielding an average of 36.5M (95% CI: 35.237.8M) reads per pool. After filtering by fastp, 98.399.5% of raw reads were retained, and 722,035,886 clean reads were used for further analyses, of which 67.5% mapped to the host genome. Reads classified as cellular organisms (including eukaryotes, bacteria, and archaea) and those with no significant similarity to any amino acid sequence were discarded, leading to 1,472,071 reads best matched with viral sequences, accounting for 0.31% of total clean reads. Due to the presence of numerous transcripts from the hosts and other organisms, most pools had low levels of viral RNA. The percentage of virus-associated reads in each pool was 0.052.47% (Supplementary Table S1).

Satellite map (left) and topographic map (right) of the rodent collection area on the eastern Tibetan Plateau of China. Shiqu county is highlighted yellow, and Sichuan province is marked in light gray, the geographic coordinate of collection site (E97443E.67", N331040.40") is marked on the topographic map. The map was generated by SuperMap (http://www.supermapol.com/).

A wide range of DNA and RNA virus groups were covered by the sequence reads. Virus-associated reads were assigned into 46 families of double-stranded (ds)DNA viruses, dsRNA viruses, retro-transcribing viruses, single-stranded (ss)DNA viruses, and ssRNA viruses (positive- and negative-strand viruses) in the virus root. Based upon natural host of each virus, we classified 13 families of these viruses as vertebrate-associated viruses (6 zoonotic viruses and 7 non-zoonotic rodent associated viruses), 11 as bacteriophages, 10 plant viruses, 6 as fungal viruses, 1 as an insect virus, 5 as eukaryotic microorganism (protozoa and algae)-related viruses and a group of unclassified viruses (Supplementary Fig. S2 and Table S2). An overview of the classified and unclassified viral reads is shown in Fig.2A.

Proportion of viral sequence reads with BLASTX hits to the specified virus families. (A) Proportion in each library. The y-axis is the percentage of viral reads distribute to each classification, or that were unclassified viruses. The sample ID is shown on the x-axis. The percentage of reads was determined based on the raw number of viral-related reads. (B) Proportion in total viral reads.

The largest proportion of the virus-classified sequences was related to vertebrate viruses, with 81.93% of the total viral reads, which included zoonotic viruses (1.4%) and non-zoonotic rodent associated viruses (80.53%). Among them, viral sequences related to ssRNA positive-strand viruses in the Picornaviridae were abundant, comprising 65.6% of the total virus-like sequence reads. The dsDNA viruses were predominantly bacteriophages such as Ackermannviridae, Autographiviridae, unclassified bacteriophages and nine other families, accounting for 12.8% of the total viral reads. In addition, 4.3% of the viral sequences were related to insect viruses (0.06%), plant viruses (2.16%), fungus viruses (0.99%), and eukaryotic microorganism viruses (1.08%) (Fig.2B). Detection of these viral sequences may be due to food consumption. In addition to the family assigned reads, 0.94% of total viral reads were identified as unclassified RNA viruses, including diverse bunyavirales, picornavirales, riboviria, and environment-related viruses. Except for unclassified virus and bacteriophage (11 families), the top 10 most widely distributed families of viruses were Picornaviridae, Flaviviridae, Retroviridae, Picobirnaviridae, Solemoviridae, Arteriviridae, Mitoviridae, Mimiviridae, Phycodnaviridae, and Reoviridae. Samples of wild Qinghai voles had marked virus diversity.

Venn analyses revealed that 21 viral families, including Picornaviridae, Flaviviridae, Retroviridae, and Picobirnaviridae, were distributed in the five tissues (Fig.3, Supplementary Table S3). However, the Venn diagram demonstrated that nine viral familiessuch as Coronaviridae, Parvoviridae, Hypoviridae, Autographiviridae and five plant viruseswere unique to feces, which indicated that these viruses have compartment specificity. In addition, six viral families were shared between intestine and feces. The other 12 viral families were found in at least two tissues, one of which was a fecal sample.

Venn diagram of viral families shared in the five tissues. The numbers represent viral families found in each tissue. A total of 48 viral taxa were analyzed and displayed, which included 46 viral families, one unclassified virus and one unclassified Bacteriophage.

The results suggest that liver and feces act as major reservoirs for diverse viruses in wild Qinghai voles, accounting for 55.3 and 26.1% of total viral reads, respectively. To detect differences in virome structures among the samples, taxonomic heatmap and hierarchical cluster analyses were conducted based on the normalized viral reads number. A heatmap of all reads to the sequences of the 46 viral families, unclassified virus and unclassified Bacteriophage identified in this study is shown in Fig.4. For instance, in liver, Picornaviridae, Flaviviridae, Iridoviridae, and Poxviridae were abundant. In lung, Herpesviridae and Arteriviridae were the most abundant virus families. The most abundant viral family in spleen was Retroviridae. In intestine, Ackermannviridae and Circoviridae were abundant. However, 37 viral families and unclassified virus were abundant in feces. Compared to the other tissues, liver and feces samples clustered together separately, which indicated a closer correlation of virome structures. Overall, our results revealed significant differences in virus composition and abundance among tissues.

Heatmap based on the distance matrix calculated by the Euclidean distance method according to the normalized number of reads belonging to each viral family in 20 pools. X axis shows sample names, and the Y axis the names of viral families. Red to blue, highest to lowest abundance of viral reads according to viral family. The hierarchical clustering is based on the Euclidean distance matrix calculated from the normalized read count. A total of 48 viral taxa were analyzed and displayed, which included 46 viral families, one unclassified virus and one unclassified Bacteriophage. The heatmap was generated by Hiplot (v0.2.0, https://hiplot.com.cn).

By characterizing host traits and transmission routes, non-vertebrate-associated viral reads, bacteriophages, and unclassified viruses reads described previously were removed. The remaining 1,206,124 viral reads (approximately 81.93% of the total viral reads) were assigned into 13 vertebrate-related viral families. Viral reads from the families Picornaviridae, Flaviviridae, Retroviridae, Picobirnaviridae, Arteriviridae, Poxviridae, and Herpesviridae were widely distributed in tissues, in different abundances. The families Reoviridae, Adenoviridae, Astroviridae, Coronaviridae, Circoviridae, and Parvoviridae were found in few tissue types. Analyses of the virus reads distribution showed that 965,703 reads (65.5% of total viral reads) exhibited sequence similarity to Picornaviridae, accounting for a major portion of the total virus reads (Supplementary Table S2). Other mammalian virus sequences in order of sequence read abundance were Flaviviridae (8.27%), Retroviridae (3.36%), Picobirnaviridae (3.27%), and other families, accounting for 1.43% of viral reads. These viruses belonged to a genus or family known to cause human or animal infection were confirmed by PCR amplification using specific primers. All these viral reads were extracted from each dataset and submitted to de novo assembly by SPAdes software, length and depth of assembly contigs were shown in Supplementary Table S4. Blast results indicated that these genomes showed low nucleotide (nt) or amino acid (aa) similarity to known genome sequences in the GenBank database. We characterized some of these full or near-full genome sequences and compared them to their closest relatives by phylogenetic analyses.

Eleven near-complete genomic sequences for Picorna-like viruses were identified in all tissues except lung. Reads related to the Picornaviridae family comprised the largest proportion of viruses, particularly in liver (85.9%), small intestine (51.5%), and feces (45.7%) samples. The distribution of these picorna-like viruses among tissues was similar to picornavirus, which infect the liver and are transmitted by the fecal-oral or blood route34,35. Overall, these 11 genome sequences of picorna-like virus were retrieved from the pools and were of 74487640bp. Using NCBIs ORF finder, it was predicted that both genomes had a single ORF encoding a polyprotein, similar to the genome structure of Hepatovirus13,36. The nt identity between contigs ranged from 99.0 to 99.9%, showing great similarity. Moreover, sequence similarity and phylogenetic analyses indicated that all contigs clustered with rodent hepatovirus. Therefore, these genomes were classified into the genus Hepatovirus (Fig.5). BLASTn search revealed that these sequences were closely related to rodent hepatovirus (KT452641.1, Myodes glareolus, collected in Germany in 2011) with nt sequence identities between 82.74 and 82.76% (Supplementary Table S4). BlastX analyses revealed that these contigs were 91.8391.88% similar at the aa level to their closet relative polyprotein, that of rodent hepatovirus (YP_009179213.1, Microtus arvalis, collected in Germany in 2010). According to the ICTV criteria, the divergence of members of hepatovirus species ranges from 0.18 to 0.40 for the P1 region and 0.190.49 for the 3CD region37. The distance was 0.030.04 for the P1 region and 0.07 for the 3CD region between these contigs and rodent hepatovirus. Therefore, these contigs were proposed to be novel variants of rodent hepatovirus.

Phylogenetic relationships of hepatovirus variants based on analyses of the P1 protein (A) and 3CD protein (B). Branch lengths are drawn to a scale of aa substitutions per site. Numbers above individual branches indicate bootstrap support, only values>80% are shown. Vole hepatovirus variants are marked by a black dot, sample ID were labeled in parentheses.

In all, 121,679 reads were assigned to the family Flaviviridae (Supplementary Table S2), being found in almost all tissues. Such a broad distribution indicates diverse modes of potential transmission, such as vertical and fecal-oral. Seven near-complete genomic sequences were identified in samples (three in liver, one in lung, one in spleen, and two in feces) by de novo assembly, with a length of 86178625bp. These sequences shared 99.299.9% identity to each other. Sequence analyses using NCBI ORF finder revealed a single ORF translated into a polyprotein, with a genome structure similar to typical Flaviviridae16,38,39. These contigs were subjected to PCR confirmation and whole-genome phylogenetic analyses. All contigs were assigned to a clade in the genus Hepacivirus with various sequence similarities to rodent hepaciviruses collected from Neodon clarkei in Tibet, China in 2014. The contigs showed 75.6375.73% nt identity and 82.8388.90% aa identity with rodent hepacivirus (Fig.6 and Supplementary Table S4). According to the ICTV guidelines, hepaciviruses with<0.25 aa p-distances in the conserved region of NS3 and 0.3 in the NS5B region belong to the same species40. Because the NS5B and NS3 region p-distances between these contigs and rodent hepacivirus were 0.16 and 0.15, they were identified as variants of rodent hepacivirus.

Phylogenetic analyses of hepacivirus variants based on the NS5B (A) and NS3 (B) protein. Branch lengths are drawn to a scale of aa substitutions per site. Numbers above individual branches indicate bootstrap support, only values>80% are shown. Hepacivirus variants are marked by a black dot, sample ID were labeled in parentheses.

In the liver, spleen, intestine, and fecal pools, 16 near-complete or partial genome sequences (0.23.3k nt) of viruses of the family Reoviridae and genus Rotavirus were characterized. Analyses using NCBI ORF finder revealed a similar genome structure to Reoviridae, including the VP1, VP2, VP3, VP4, VP6, VP7, NSP2, and NSP3 segments38,41,42. BLASTn analyses of seven PCR-amplified segments (two of VP1, two of VP2, and three of VP3) revealed that vole rotavirus was related to other viruses from a range of host species, including Lama guanicoe, chicken, Rhinolophus blasii, Microtus agrestis, and human, with nt similarities of 70.8176.78% and aa identify of 67.6786.85% to the closest relatives in the VP1, VP2, and VP3 segments (Supplementary Table S4). These findings were confirmed by the phylogenetic analyses of the VP1 and VP6 segments. The contigs clustered with the species rotavirus A (Fig.7). According to the aa sequence identities of the RdRp (VP1) and VP6 regions, these contigs were proposed to be novel variants or genotypes of rotavirus A43.

Phylogenetic relationships of vole rotavirus A based on the VP1 (RdRp) protein (A) and VP6 protein (B). Branch lengths are drawn to a scale of aa substitutions per site. Numbers above individual branches indicate bootstrap support, only values>80% are shown. Novel rotavirus A variants are marked by a black dot, sample ID were labeled in parentheses.

In this study, 90% of picobirnavirus (PBV) sequence reads were detected in fecal samples. Two PBV contigs were obtained and PCR-confirmed from two fecal pools, with lengths of 1685/1684bp. The distributions of these sequences were coincident with other PBVs, which have been detected in the feces of human, rabbit, dog, pig, rat, and bird5,41. Further analyses of these two segments revealed 2 RdRp region of PBV. These two segments showed low similarity to PBV sequences in GenBank. Based on the best RdRp matches from a BLASTn and BLASTx search, and several related strains from GenBank, nucleotide and protein phylogenetic trees were constructed separately. The two segments clustered with PBVs detected in fecal samples of rat collected in China, with 81.4% nt identity and 81.2% aa identity, respectively (Fig.8 and Supplementary Table S4). According to the ICTV guidance, the high similarly between RdRp and Rat PBV revealed that these segments are new variants of PBV44.

Phylogenetic analyses of picobirnavirus genomes on the basis of the segment 2 (RdRp) aa sequence. Branch lengths are drawn to a scale of aa substitutions per site. Numbers above individual branches indicate the bootstrap support, only values>80% are shown. The novel variants of picobirnavirus are marked with a black dot, sample ID were labeled in parentheses.

Other sequence reads or contigs related to mammalian viruses showed low nucleotide and amino acid sequence identities (<80%) with known viruses. Of 13 vertebrate-associated viruses identified, 9 were selected (Supplementary Table S4) for confirmation by PCR screening and Sanger sequencing. In addition to hepatovirus, hepacivirus, rotavirus, and PBV, astrovirus were verified in fecal samples. The assembled Astrovirus contigs with length of 242343bp showed 6978.9% nt identity and 64.676.3% aa identity to diverse Astrovirus.

Moreover, some sequence reads related to the families Coronaviridae, Circoviridae, Parvoviridae, and Arteriviridae were occasionally detected and confirmed by RT-PCR. However, these segments were too short to identify genotypes, this suggests that these viruses might be of low viral load. Among them, coronavirus contigs were detected only in the fecal library (275 and 249bp), and showed similarity to a known rodent coronavirus strain, Lucheng Rn rat coronavirus (MT820627.1), belonging to the genus Alphacoronavirus, with 87.94% nt identity and 91.21% aa identity. The circovirus contigs from the intestine and fecal libraries (367bp) showed 78.11% nt identity to a feline cyclovirus (KM017740.1). Some contigs related to the family Parvoviridae were also identified, showing 74.86% similarity at the nt level and 73.75% at the aa level to a murine bocavirus (NC_055487.1). Sequence reads of Arteriviridae were identified in lung and spleen, one contig was retrieved from spleen (350bp) showed 70.6% nt identity and 73.9% aa identity to Arteriviridae sp., which was detected in Mus pahari in Thailand (MT085142) (Supplementary Table S4).

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Metagenomic analysis of viromes in tissues of wild Qinghai vole from the eastern Tibetan Plateau | Scientific Reports - Nature.com

Job description

The Jackw lab has an opportunity for a Research Assistant (RA)to join their Molecular and Genome Editing Therapeutics Group at Kings College London. The post, which is a 1.0 FTE fixed term contract until the 31st August 2024. The successful candidate will take charge of handling skin biopsies and blood samples from patients to isolate skin and blood cells, culture and bank those cells. In addition, preparation of the media, reagents, as well as histology and sectioning and staining of the skin equivalents will be part of the responsibility of the RA. The technical support for this project is invaluable and will be essential to move gene editing projects in our laboratory forward. The successful candidate will be helping PhD students, masters students and post docs in the lab in gene editing and other related projects. Recessive dystrophic epidermolysis bullosa (RDEB) is a currently incurable inherited skin disorder characterised by severe blistering of the skin. We have demonstrated the feasibility of using gene editing technology for developingex vivoorin vivogene therapy for patients with inherited skin disorders like RDEB. This project will bring new insights into potentialin vivotherapy using base or prime editing types of gene editing in combination with nanotechnology.

This new RA position will build on previous work from the lab to biobank more patient samples, characterize them and make them ready for gene editing testing using different strategies under investigation in the laboratory (Jackow et al., 2019 PNAS; Sheriff et al., 2022 Manuscript in Revision).

The project will have access to skin biopsies from patients with different forms of genodermatoses including epidermolysis bullosa from our clinic at the St Johns Institute of Dermatology, the largest tertiary referral Centre in the UK for RDEB.

The post will be based at Guys Campus within the School of Basic and Medical Biosciences at St Johns Institute of Dermatology. The candidate will work closely with the principal investigator, Dr Joanna Jackw and Prof Stephen Hart from UCL who is an expert in nanoparticle delivery. We will also closely work with Prof John McGrath and Prof Jemima Mellerio, both of whom are world-renowned experts in epidermolysis bullosa and will be the key clinical personnel to guide patient selection and provision of samples. The postholder will also benefit more broadly from the vast interdisciplinary research and academic networks at Kings College London.

The successful candidate should have a Bachelor or Masters degree in at least one natural science subject. The candidate should be proficient in cell culture and competence working with cells (cell lines, primary cells and iPSCs). Experience with phenotyping of cell subsets using different methods such as: functional assays (proliferation, differentiation, migration and 3D skin constructs), molecular assays (PCR, western blotting, flow cytometry, electroporation) will be beneficial but are not essential. Experience with organoid culture and skills in bioinformatics analysis will be highly valued but is not a prerequisite for this position. In addition, some knowledge and experience in skin biology will be of benefit.

This post will be offered on an a fixed-term contract till 31/08/2024

This is a full-time post - 100% full time equivalent

Key responsibilities

The successful applicant will be responsible for:

The day to day running of the cell culture including occasional weekend work.

Handling skin biopsies, logging in the samples, keeping an update on all log books and being in charge of all spreadsheets related to it.

Isolating fibroblasts and keratinocytes from skin biopsies, and peripheral blood mononuclear cells and other immune cell types from blood.

Maintaining stock levels of consumables Conducting experiments, analysing data and presenting findings to colleagues and supervisors.

Assisting PhD students and master's students within the department.

Working towards meeting project milestone deadlines.

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.

Skills, knowledge, and experience

Essential criteria

The applicant must have:

1. A Bachelor or Masters degree in a relevant area

2. Molecular biology and cell culture experience or research in a relevant area

3. An enjoyment for research at the bench and cell culture laboratory

4. Experience usingin vitro2D and 3D models; ideally relating to skin

5. Be skilled at keeping detailed, trackable notes and records

6. Be competent in different techniques one has to apply: flow cytometry and its analysis, western blot, PCR, gel electrophoresis, immunocytochemistry, qPCR

Desirable criteria

1. Skilled in bioinformatics analysis and interpretation of the data

2. Experience with ethical approvals for human subjects. Authorship on peer-reviewed publication

Further information

Interviews will be held in person/remotely by the end of October, 2022 and will include a 10 minute presentation of a previous research project that the applicant has conducted.

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Research Assistant in Molecular and Genome Editing Therapeutics job with KINGS COLLEGE LONDON | 311876 - Times Higher Education