Category Archives: Genome

Mammalian DNA is folded in 3D structures that create different neighborhoods in the genome. These sections of DNA, formally called topologically associating domains, remain insulated from each other in order to control how genes get expressed. But when a piece of DNA in one neighborhood is required to control and develop a unique set of genes in another, the neighborhoods must then intermingle.

According to a study led by Golnaz Vahedi at the Perelman School of Medicine, one protein, called TCF-1, allows various parts of these otherwise insulated DNA to mix in way thats required for the T cellsa key element of the bodys immune systemto develop. The role this protein plays in T cell creation could shed new light on immunotherapy approaches. The team published its findings in Nature Immunology.

By studying the mechanics of the protein TCF-1 and how it reconfigures the genome, Vahedi, an associate professor of genetics and a member of the Penn Institute for Immunology and Penn Epigenetics Institute, and colleagues, discovered that the TCF-1 protein has a unique ability to enable plasticity in cells across neighborhoods during the development of T cells.

These domains, or insulated neighborhoods, are like stickers for social distancing, Vahedi says. They essentially say, Stay awaykeep a certain distance apart. But what this protein does is to remove these stickers and say, You can now actually intermingle. It disrupts the spatial distancing.

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One protein's role in genomic intermingling and T cell development | Penn Today - Penn Today

Anyone who reads even a little about science and technology will be familiar by now with the idea of genome sequencing. This process involves breaking an organisms DNA into fragments to study their compositions or sequences. Then the fragments are aligned and merged to reconstruct the original sequence.

But why sequence an organisms genome? Whats the value for ordinary people and the world more broadly? The answers are immediately obvious when it comes to the medical field. Understanding what makes a disease tick offers scientists a way to treat or prevent it. Sequencing the genome of a crop or animal can improve agricultural yields or make species hardier in shifting climates.

Its a little tougher to explain the value of sequencing the genome of plant pathogens, the organisms that cause diseases in plants. But this has become a critical part of the work of microbiologists and plant pathologists. And it is important, far beyond the laboratory: by carefully studying plant pathogens genomes, researchers have been able to design specific double stranded RNA fungicides to short circuit some pathogens abilities to harm plants.

These fungicides have not yet been deployed commercially but have huge potential only targeted species will be affected and so the process is likely to be more environmentally friendly than any involving chemical fungicides. This research has the potential to protect crops, benefiting agriculture and contributing to food security.

For the past 13 years Ive focused on sequencing one plant pathogens genome. Heres where that scientific journey has led.

I sequenced the genome of a fungus called Fusarium circinatum in 2009; it was the first fungal genome sequence to be conducted on the African continent.

I started studying this pathogen more than 20 years ago because it was killing seedlings in South African pine nurseries. Fusarium circinatum causes pitch canker on pine trees, which makes trees exude pitch or resin. In severe cases the fungus causes tree death. This fungus is considered to be the most important pathogen threat to the global plantation pine industry. It is also potentially devastating in some areas of the southern US, Central America, Europe and Asia, where pines are found naturally.

Trees are extremely important in carbon sequestration. They also produce oxygen it is estimated that, daily, one tree can produce enough oxygen for four people. Trees have huge economic value, too, providing timber for our homes and paper and packaging for many uses in our daily lives. It is difficult to estimate the total value of pine plantations globally but the South African industry is estimated to contribute more than US$2 billion to the countrys Gross Domestic Product annually.

Sequencing the genome was just the beginning. Follow-up studies published in 2021 involved knocking genes out of the genome and studying what happened. This process is a bit like first identifying and lining up all the parts, then removing these parts one at a time to see what difference they make to the functioning of the fungus. Sometimes we need to understand how gene products (proteins) interact with each other and then more than one gene might be removed from a genome.

In this way, my colleagues and I can learn which genes are important to the processes that Fusarium circinatum uses to cause pitch canker and which are not. Now were working to target the important genes in studies to manage the pathogen.

Its time-consuming work: this fungus has around 14,000 genes. This is more than the yeast that is used to ferment beer, which has 6000 genes, but less than the estimated 25,000 genes in the human genome. Luckily technologies are evolving rapidly to enable routine gene knock-outs. This involves a protein which acts a bit like DNA-specific scissors allowing deletion of a specific sequence of DNA. The position where the protein cuts is guided by using small pieces of RNA sequence that are identical to the target DNA sequence.

Read more: What is CRISPR, the gene editing technology that won the Chemistry Nobel prize?

Another of our key findings is that Fusarium circinatum has acquired, through horizontal gene transfer from other organisms, a group of five genes that apparently enhance its growth.

This discovery has been very useful in developing a specific diagnostic tool using LAMP PCR (Loop-mediated isothermal amplification) to identify this pathogen. This is a special kind of highly sensitive test that was developed to allow for in-field detection of pathogens. It also doesnt require specialised training. This is useful because trees only recently infected with Fusarium circinatum can be asymptomatic. Its crucial to determine the presence of the pathogen as early as possible so its spread can be better managed.

The rise in studies that sequence plant pathogens genomes has also opened up opportunities for scientists to develop new skills. The data generated by genome sequencing sometimes outstrips the number of researchers available to analyse it. During pandemic lockdowns in South Africa, some students in my research programme learned how to code and developed skills in bioinformatics, using computers to capture and analyse biological data rather than working in a laboratory.

With these new skills, as well as fast-improving technology, we may well crack Fusarium circinatums code once and for all. And that will help to guard pine trees against a dangerous, costly pathogen.

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Following a fungus from genes to tree disease: a journey in science - The Conversation

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The genomic basis of the plant island syndrome in Darwin's giant daisies - Nature.com

The clinical course of the A. baumannii infection and phage treatment, known as the Patterson Case, has been described previously11. Briefly, phage treatment was initiated with two-phage cocktails, each containing four phages: cocktail PC was administered into abdominal abscess cavities through existing percutaneous drains, and cocktail IV was administered intravenously. Near the end of patient treatment, a ninth phage, AbTP3phi1, was isolated to target the phage-resistant A. baumannii strain TP3 that arose during treatment. Phage AbTP3phi1 was administered intravenously in a two-phage cocktail (IVB) in combination with one phage from cocktail IV11. As a follow up study to this phage intervention case, we determined the genomes of the phages and also of the bacterial strains that were isolated during phage treatment.

All nine phages used in the treatment cocktails were sequenced to completion and their genomes are summarized in Table1. Genome sequences of phages C2P12, C2P21 and C2P24 described as part of cocktail PC11 were determined to be identical, so phage C2P24 was renamed as phage Maestro and is used as a representative of this group. The phages described here can be categorized into two broad groups: phages Maestro, AC4, AB-Navy1, AB-Navy4, AB-Navy71 and AB-Navy97 are large (165169kb) T4-like myophages, and phage AbTP3phi1 is a 42kb Fri1-like podophage.

The six myophages can be subdivided into two clusters, with Maestro and AB-Navy71 sharing 91.6% identity and phages AC4, AB-Navy1, AB-Navy4, AB-Navy97 sharing from 93.2% to 95.5 identity (Fig.1A). Based on their sequence relationships to other A. baumannii phages and the criteria of the International Committee on Taxonomy of Viruses (ICTV)17, Maestro forms a new species in the genus Hadassahvirus, and phages AC4 and AB-Navy4 each form new species in the genus Lazarusvirus (Table1). Phages AB-Navy1, AB-Navy71 and AB-Navy97 can be assigned to species in the genus Hadassahvirus or Lazarusvirus (Table1). All of these phages are members of the Subfamily Twarogvirinae within the Family Straboviridae, which also contains the broadly-defined T4-like myophages, including the coliphage T4 itself. The 42kb podophage AbTP3phi1 is classified as a new species within the genus Friunavirus of the Subfamily Beijernickvirinae (Table1). It shares 8289% overall DNA identity, as well as genome synteny, with previously described Acinetobacter podophages, including IME200 (NC_028987), vB_AbaP_AS11 (NC_041915), Fri1 (KR149290)18 and Aci08 (NC_048081).

A DNA sequence relatedness of six T4-like myophages, showing pairwise percent DNA sequence identities as determined by ProgressiveMauve (upper section) and DNA dotplots visually representing DNA sequence alignments between phages (lower section). B Protein sequence-based relationships of 2834 Caudoviricetes phages representing all species in the ICTV taxonomy, plus the seven treatment phages and two prophages identified in strains TP1, TP2, and TP3. Unclustered singletons (17 in total) are removed from the visualization. Distinct colors are assigned at the Subfamily level; if no Subfamily was assigned, color is assigned at the Family level. Circled clusters are enlarged in CE as labeled. C Enlarged cluster representing the Autographiviridae. Nodes are colored based on their Subfamily membership, with the legend identifying prominent clades; the node representing phage AbTP3phi1 is outlined in black, and nodes representing clade-founding phages T7, phiKMV and Fri1 are labeled. D Enlarged cluster containing the T4-like subfamilies, including the Twarogvirinae; this large cluster is also linked to the T5-like Markadamsvirinae and clusters of diverse myophages including the V5-like Vequintaviridae and FelixO1-like Ounavirinae. Nodes are colored based on their Subfamily membership with the legend identifying prominent clades. Nodes representing the six treatment myophages are outlined in black, and nodes representing clade-founding phages T4, T5, FelixO1 and V5 are labeled. E Enlarged cluster containing the two prophage elements identified in strains TP1, TP2 and TP3. These prophages are not closely related to other classified phages, with the 52kb prophage 1 distantly linked to the Guernseyvirinae, and the 42kb prophage 2 related to two other unclassified siphophages.

Phage taxonomy is rapidly evolving, with multiple major recent and proposed revisions to the organization of phage taxa based on genomic relationships19. A recent global analysis of 134 Acinetobacter phage genomes placed these into eight major clusters and 38 sub-clusters, which include five proposed new subfamilies and 30 new genera20. The most abundant groups in this analysis are members of the Twarogvirinae and the Beijernickvirinae, with the vast majority of the latter (45/49 phages) falling into a single genus, the Friunavirus. A comparison of the seven treatment phages to a database constructed from species representatives of the Caudoviricetes (tailed dsDNA phages) in the ICTV taxonomy (Fig.1B) shows higher-order relationships and diversity of these phages. This analysis produced a major grouping representing the Autographiviridae, which contains AbTP3phi1 (Fig.1C) and a grouping containing the Twarogvirinae and Tevenvirinae, containing the six treatment myophages (Fig.1D). This analysis placed the five current ICTV Twarogvirinae genera into three sequence-based viral clusters, with the treatment phages placed in a viral cluster with the other members of the genera Hadassahvirus and Lazarusvirus. Likewise, all members of the genus Friunavirus were placed into a single viral cluster with AbTP3phi1.

The genome of Maestro is presented as a representative for this group of Acinetobacter myophages (Supplementary Fig.1). Maestro has a complete genome size of 169,176bp and a GC content of 36.6%. Seven tRNA genes were identified, including one that appears to specify an amber codon. Genes encoding phage integrases or proteins associated with bacterial virulence were not detected. A conserved core of 95 genes encoding proteins with direct identity to coliphage T4 (BLASTp, E<105) were identified, clustered in several regions of the genome. These include genes encoding structural proteins and proteins involved in DNA nucleotide metabolism and replication. Proteins involved in transcriptional regulation in phage T4 were found to have homologs in Maestro, which suggests Maestro follows a T4-like program of gene expression, with positive control of early, middle and late transcripts21. The holin and endolysin lysis genes in Maestro are similarly located as in T4 and have high primary structure similarity, indicating that the first two steps in lysis, the permeabilization of the inner membrane and the degradation of the cell wall are effected the same way22. The third step, disruption of the outer membrane, is accomplished in most dsDNA phages by spanin proteins23. No candidate spanins were detected in the Maestro genome, indicating that Maestro, like some other Acinetobacter phages, uses a different mechanism for OM disruption23,24. Homologs of the phage T4 RI and RIII antiholins were identified in the Maestro genome, indicating this phage has the ability to undergo T4-like lysis inhibition25. The effects of lysis inhibition in therapeutic interventions are not known, but superinfection-induced lysis inhibition delays lysis time and increases burst size in vitro and could affect in vivo phage proliferation at the site of therapeutic application. An analysis of 16 Twarogvirinae species representatives including Maestro, AB-Navy4 and AB-Navy97 by CoreGenes26 showed that 129 genes are conserved in this group, which includes the major DNA metabolism and structural functions, and a set of 41 hypothetical proteins with no identified function (Supplementary Fig.1).

During the infection process of phage T4, the long tail fibers (LTFs) bind to the phages receptor on the cell surface. In T4, the LTF is comprised of Gp34, Gp35, Gp36 and Gp37, which form the proximal LTF, two joints, and distal LTF, respectively; the distal LTF contains the phage receptor-binding function in its C-terminal domain27,28. The distal domains of the myophage LTFs, containing the predicted receptor-binding domains, were compared by multiple sequence alignment (Supplementary Fig.2) and construction of a neighbor-joining tree to determine their relationships (Fig.2). This analysis showed the myophages used in the cocktails had two different types of tail fibers, with Maestro, AC4, and Navy71 belonging to one group, and Navy1, Navy4, and Navy97 belong to the other cluster (Fig.2). This finding correlates with the phage resistance patterns observed in A. baumannii strains isolated from the patient before and during phage treatment (Table2). Strains resistant to phage AC4 were also resistant to phage Maestro and AB-Navy71, but the same strains were still partially sensitive to AB-Navy1, AB-Navy4, and AB-Navy97. Six days after the start of treatment, resistance to AB-Navy1, AB-Navy4 and AB-Navy97 was observed simultaneously. The closer relationship of the AC4 tail fiber to Maestro and AB-Navy71 likely represents a horizontal gene transfer event, as AC4 is overall more closely related to phages AB-Navy1, AB-Navy4 and AB-Navy97 (Fig.1), and this points out a limitation of using whole-genome comparisons to predict the behavior of individual phages.

The tail fibers of phages Maestro, AC4, and AB-Navy71 form one clade and the fibers of AB-Navy1, AB-Navy4 and AB-Navy97 form another.

The podophage AbTP3phi1 shows conserved protein content and synteny with other members of the Friunavirus genus, which is part of the larger group Autographiviridae that also includes the well-studied E. coli podophage T7. The genome map of AbTP3phi1 is shown in Supplementary Fig.3. As a conserved feature of this group of phages, a terminal repeat region of 396bp was identified in AbTP31 genome by the PhageTerm tool29. Like T7, these phages possess relatively small genomes of ~40kb and encode all proteins on one strand. Unlike T7, the gene encoding the RNA polymerase is located near the center of the genome, just upstream of the gene encoding the head-tail connector, an arrangement that is similar to that of phage phiKMV30. A CoreGenes analysis of 16 Friunavirus species representatives including AbTP3phi1 indicated that 29 out of 56 AbTP3phi1 protein-coding genes were conserved, which includes the DNA primase, helicase, ligase, polymerase, exo- and endonuclease, capsid, internal virion proteins, lysis proteins, the small and large terminase, and nine hypothetical proteins (Supplementary Fig.3). Not conserved are a number of hypothetical proteins (mostly located near the left end of the genome) and the C-terminal portion of the tailspike, which contains capsular depolymerase activity31. Like other known A. baumannii Fri1-like podophages, tail spike protein of AbTP3phi1 contains a pectate lyase fold (PF12708) and thus uses the bacterial capsule as its receptor, degrading the bacterial exopolysaccharide as part of its infection process20. Strains TP1, TP2 and TP3 all encode KL116 capsule loci as determined by Kaptive32, thus the AbTP3phi1 depolymerase is presumed to be active against this capsule type. As with the myophages reported in this study, spanin proteins were not found in the genome of AbTP3phi1 nor in any other A. baumannii podophage genomes24, suggesting the presence of a novel strategy for disruption of the outer membrane in these phages.

During phage treatment, A. baumannii isolates were collected from the patient via various drains or bronchial washes. These strains were tested for their phage sensitivity via plaque assays. These showed that as early as 2 days after phage administration, the efficiency of all the phages in the first two cocktails (PC and IV) was reduced when tested against the bacterial strains isolated during treatment, evident by the decreased titers on those strains compared to the initial titers observed with TP1 (Table2). In some cases, only a zone of clearing (but no individual plaques) was observed on the plates at high phage concentrations. Consistent with the myophage tail fiber protein sequence alignment (Fig.2), host resistance to phages appeared earlier with Maestro, AC4, and Navy71 as a group, and later with phages Navy1, Navy4, and Navy97 as a group. In comparison, resistance to phage AbTP3phi1 was not observed in bacterial isolates collected throughout 2 months of phage treatment, although plating efficiencies of AbTP3phi1 varied by up to three orders of magnitude on strains collected during treatment (Table2). The emergence of phage resistance early in phage treatment illustrates the potential benefits of well-characterized and rationally designed phage cocktails in treatment, which could be designed to mitigate the emergence of resistance. It also raises questions on the benefits of continued phage treatment beyond the first ~9 days, since all isolates collected after this time are fully resistant to the phage. While it is possible that the prolonged period of phage administration (over 60 days) was not required to produce the observed clinical outcome, other studies have shown that phage-insensitive mutants of A. baumannii exhibit reduced virulence33,34,35, a phenotype that has also been observed in other systems including Staphylococcus aureus36, Klebsiella pneumoniae37 and P. aeruginosa38. Thus, maintaining selection pressure for the phage-resistant phenotype may provide a benefit to continued treatment even after the pathogen has developed resistance to the treatment phage.

Some strains isolated throughout phage treatment were also tested for their antibiotic resistance profiles by traditional microtiter MIC (Supplementary Table2). At the time TP1 was isolated from the patient, they were receiving a combination of fluconazole, azithromycin, colistin, and rifampin. Shortly after TP1 isolation, meropenem was added to treatment, and shortly after the beginning of phage administration the azithromycin, colistin, and rifampin were discontinued and minocycline was initiated. The meropenem, minocycline and fluconazole treatment was continued through the end of phage treatment11. In general, the antibiotic resistance profiles of all strains isolated during the course of phage therapy remained consistent, indicating that phage therapy did not have a major impact on antibiotic resistance of the pathogen in this case. Although an initial report indicated resistance to colistin and tigecycline prior to the start of phage therapy11, sensitivity to colistin and tigecycline (in the range of 28ug/ml) was observed in strains isolated ~7 weeks after the start of phage therapy (collected on May 9, 2016). While sensitive to colistin and tigecycline, these strains were resistant to minocycline. We previously reported on a potential synergistic in vitro activity between phage cocktail and minocycline (used at sub-inhibitory concentrations of 0.25ug/ml) in inhibiting bacterial growth11. However, such results were obtained using strain TP3, and TP3 was not tested for its sensitivity to minocycline, colistin, or tigecycline in these MIC assays. Increased antibiotic sensitivity has been associated with phage resistance in organisms such as A. baumannii34,35 and P. aeruginosa39. However, some studies have observed increased antibiotic resistance in phage-resistant mutants40,41, indicating increased sensitivity to antibiotics is not a universal outcome from phage resistance and is probably dependent on the host, drug, phage, and nature of the resistance mutation. While fitness costs can be associated with phage resistance, the effects of resistance mutations can be pleiotropic with phenotypes that are not always easily predictable42.

To more fully delineate the phenotypic differences between TP1 and TP3, BioLog phenotypic microarray (PM) profiling was conducted using PM 120 (Fig.3 and Supplementary Data1). As expected given the clonal nature of the isolates, the PM demonstrated very consistent phenotypes in terms of carbon, nitrogen, phosphorus and sulfur utilization; biosynthetic pathways and nutrient stimulation; osmotic/ionic response; and pH response; as well as very consistent phenotypes in the chemical sensitivity assays (Fig.3). The phenotypic profiling results show that growth of both isolates TP1 and TP3 could be inhibited by colistin or minocycline at higher concentrations (Fig.3, yellow box and light blue box, respectively); tigecycline sensitivity is not included in the phenotype microarray (PM) panel. Isolate TP3 was found to be completely resistant to nafcillin in this assay, whereas TP1 was sensitive (Fig.3, purple box).

Each row represents a bacterial isolate (TP1 or TP3) in one phenotype panel (PM01PM20), and each column represents a specific condition (wells A01-H12) within each panel, as shown in Supplementary Data1. Effects on bacterial metabolic activity are indicated by color, with red representing growth inhibition, black representing intermediate growth and green representing growth promotion. Yellow box: colistin. Light blue box: minocycline. Purple box: nafcillin. Results are calculated using the area under the curve for 48h of growth and are presented as the average of three replicates per strain.

Three A. baumannii isolates, TP1, TP2, and TP3, were sequenced to closure using a combination of short-read (Illumina) and long-read (Nanopore) sequencing to investigate pathogen evolution during the course of phage treatment. Sequencing to closure allows for tracking of the number and position of mobile DNA elements that are often not assembled into larger contigs if the genomes are only sequenced with short-read sequencing. Strain TP1 was isolated prior to the start of phage treatment and was the clinical isolate used to determine phage sensitivity and conduct environmental phage hunts for assembly of therapeutic phage cocktails11. Strains TP2 and TP3 were isolated 6 days and 8 days after the start of phage treatment. All three strains were found to contain a single 3.9Mb chromosome and a single 8.7kb plasmid (Table3). Some variation was observed in bacterial chromosome length between strains but the plasmids contained in each strain were identical, and it is clear that these three isolates represent the evolution of strains from a common ancestor over time rather than a succession invasion by different strains. Analysis of the genomes in pubMLST43 identified all three isolates as sequence type 570 (Pasteur) and analysis in Kaptive44 identified a 20.5kb region (base position 3,774,0313,794,556 in the TP1 genome) containing 17 genes encoding a predicted capsule type of K116 (Supplementary Table3). Consistent with the broad antibiotic resistance observed in these isolates, 32 (TP1) and 35 (TP2, TP3) antibiotic resistance genes (ARGs) were identified based on searches against the CARD 2021 database45 (Supplementary Data2). The 8.7kb plasmid contained in TP1, TP2 and TP3 does not encode any identifiable AMR genes, and is identical to plasmids carried in many other A. baumannii strains deposited in NCBI. Few SNPs and indels were observed between these isolates, including 23 large (>1kb) insertions or deletions associated with the movement of mobile DNA elements. Summaries of the genomes and changes observed in strains TP2 and TP3 (relative to TP1) are summarized in Table3, and the locations of AMR genes, transposases, prophages, capsule locus in TP1 genome, and large insertion and deletions (>1kb) in TP2 and TP3, in reference to TP1, are illustrated in Fig.4. In reference to TP1, detailed sequence changes, associated coordinates and genes affected in TP2 and TP3 are listed in Supplementary Tables4 and 5, respectively.

Each gray band represents a bacterial chromosome as labeled at the replication origin. The scale on the outer ring represents DNA coordinates in Mb. The locations of AMR genes, transposases, prophages and the capsule locus are indicated for TP1 only. Major insertions or deletions (>1kb) are indicated in the TP2 and TP3 chromosomes. Of note, TP2 and TP3 contain a novel 6.7kb insertion element at the ~0.11Mb position that is not present in TP1, indicating horizontal acquisition during infection.

The most notable change in TP2 and TP3 is the acquisition of a novel 6673bp insertion sequence, inserted in a position adjacent to an existing IS3-like transposase at position 111,357 of the TP1 genome (Fig.4 and Supplementary Tables4 and 5). This acquired 6.7kb element is not native to TP1 and represents an acquisition of new DNA by horizontal gene transfer that occurred during the course of infection, and is most likely the result of DNA acquisition mechanisms unrelated to phage treatment. A. baumannii is known for its ability to rapidly acquire mobile DNA elements in the environment via conjugation and natural competence46,47, and to vary surface molecules through horizontal gene transfer48. T4-like phages like those used in treatment are generally poor transducers. In phage T4, multiple defects in ndd, denB, 42 and alc are required for transduction to occur49, and these genes are all conserved in the cocktail myophages reported in this study. In addition, transduction requires the phage to be able to productively infect the donor of the acquired DNA, which was likely to have been a different bacterial species and thus insensitive to the phages used. BLASTn searches of this sequence identified identical or nearly identical sequences in other Gram-negative bacterial genomes or plasmids, including A. baumannii (CP038644), Klebsiella pneumoniae (LR697132), E. coli (CP020524), and Citrobacter freundii (KP770032). This inserted sequence encodes a number of significant additional antibiotic resistance determinants, including a predicted aminoglycoside O-phosphotransferase (IPR002575), an NDM-1-like metallo-beta-lactamase (CD16300, IPR001279), and a CutA-like protein that may be involved in metal tolerance (IPR004323). The inserted aminoglycoside O-phosphotransferase (CARD ARO:3003687) is relatively rare in A. baumannii, found in 1.43% of A. baumannii chromosomes and 0.47% of A. baumannii plasmids, as reported by the CARD Resistance Gene Identifier. The prevalence of the inserted NDM-1-like metallo-beta-lactamase (CARD ARO:3000589) is 5.94% of A. baumannii genomes and 0.6% of A. baumannii plasmids.

Other than the 6.7kb insertion described above, all other major variations in the TP2 and TP3 genomes can be attributed to deletion or transposition of elements present in the TP1 genome (Supplementary Tables4 and 5). Another 1886bp insertion sequence was identified in TP2 and TP3 which introduces a second copy of the IS6-like transposase and an additional copy of an aminoglycoside O-phosphotransferase (IPR002575) which is also present in TP1 (locus HWQ22_16890). In this case, this insertion is a duplication of an existing AMR gene rather than the acquisition of foreign DNA. The presence of the new 6.7kb element and the duplicated 1.9kb element resulted in three extra ARGs in TP2 and TP3 (35 total predicted AMR genes) compared to TP1 (32 total predicted AMR genes) (Table3 and Supplementary Data2). This highlights the fact that bacterial pathogens do not exist as strictly clonal populations even in a single patient over time.

Prophage analysis revealed two apparently complete prophage regions (52,563bp and 42,762bp in length, respectively) in TP1, TP2, and TP3 genomes that are likely to encode active prophages (Fig.4). Phage att sites and conserved phage proteins (tail and tail tape measure protein, major head subunit and head morphogenesis protein, terminase large subunit, endolysin) were identified in both prophage regions; the coordinates of the prophages in the TP1 genome are provided in Supplementary Table3. These two prophage regions are conserved in TP1, TP2, and TP3 and no sequence change was observed among the three strains. The 52kb prophage 1 is highly conserved (with up to 100% nucleotide identity by BLASTn) in many other A. baumannii genomes, including that of ATCC 19606, which is one of the earliest available clinical isolates of A. baumannii dating to the 1940s50. This prophage region shares limited similarity to cultured phages, with its closest relative being Acinetobacter phage Ab105-3phi (KT588073), with which it shares 49.4% nucleotide identity and 22 similar proteins. The 43kb prophage region was found to be less conserved in other A. baumannii genomes, with the most closely related prophage element sharing only 69% overall sequence identity. This prophage region is ~46% related to A. baumannii phage 5W (MT349887), which also appears to be a temperate phage due to the presence of an integrase and LexA-like repressor. Other than 5W, this element is not closely related to any other cultured phages in the NCBI database, sharing no more than 10% nucleotide identity and no more than 8 proteins with other phages. Protein-based clustering of these elements (Fig.1E) showed that they are only distantly related to other cultured phages, with the closest neighbors in the Guernseyvirinae. A recent analysis of prophage carriage in A. baumannii genomes suggests that intact prophages are relatively uncommon in this species (less than one per genome) and also highly diverse, indicating a large amount of unexplored diversity in temperate phage elements51.

Five phages selected from the phage cocktails (AC4, Maestro, AB-Navy1, AB-Navy97, AbTP3phi1) were used to select for phage-insensitive mutants in vitro using A. baumannii strain TP1 as host. Three independent mutants against phages AC4, Maestro, AB-Navy97, AbTP3phi1 were isolated, and two independent mutants against phage AB-Navy1 were isolated. After resequencing and mapping mutant reads to the reference TP1 genome, changes detected with quality scores greater than 100 were examined (Table4). The majority of identified mutations were located in the bacterial KL116 capsule locus. The K116 capsule is comprised of a five-sugar repeating unit with a three-sugar backbone composed of Gal and GalNAc and a two-sugar side chain composed of Glc and GalNAc52. In all the mutants resistant to the myophages Maestro, AC4, AB-Navy97, a common 6-bp deletion was observed in a predicted capsular glycosyltransferase protein identified as Gtr76 by Kaptive (HWQ22_04225) (Fig.5). Notably, these 6-bp deletions were also observed in isolates TP2 and TP3, which evolved in vivo during phage treatment and were insensitive or showed reduced sensitivity to all myophages tested (Table2 and Supplementary Tables4 and 5). These 6-bp deletions occurred in a region containing four copies of a tandem repeat sequence TAAATT (Fig.5B), which probably is prone to mutation by strand slippage events during replication. These mutations result in the deletion of residue L243 and N244, resulting in the reduction of a predicted flexible linker between two -helices in the C-terminus of the glycosyltransferase protein. This protein is predicted to participate in capsule synthesis by forming the -D-GalNAc-(14)-D-Gal linkage of the side chain disaccharide to the trisaccharide backbone52, suggesting that this side chain plays a role in host recognition by these phages.

A Diagram of the KL116 capsule locus identified in strains TP1, TP2, and TP3 as predicted by Kaptive. Genes are represented by arrows oriented in the direction of transcription. Orange arrows represent genes involved in capsule export, yellow genes are involved in repeat unit processing, blue genes are involved in simple sugar biosynthesis, green genes encode glycotransferases and the red gene codes for the initiating transferase. All genes had 100% coverage and ranged from 90100% identity to the KL116 type in the Kaptive database. Defective capsule locus genes identified inin vitro-generated phage-insensitive mutants of TP1 are indicated by black arrows; numbers in parentheses after each phage name indicate what proportion of phage-insensitive mutants contained a mutation in that gene. B Nucleotide alignment of the sequences showing the six nucleotide deletion in one of the glycosyltransferases (gtr76) found in multiple TP1 mutants resistant to the cocktail myophages.

In mutants selected for insensitivity to phage AB-Navy1, one mutant contained the same conserved 6bp deletion identified in the other mutants, and one lacked this mutation but instead had a nonsense mutation (W183am) in carO (HWQ22_09280) (Table4). CarO is a 29kDa outer membrane transporter, loss of which has been associated with increased antibiotic resistance53,54. The role of CarO in phage sensitivity is not clear, but its truncation may lead to other cell wall defects that reduce sensitivity to this phage; truncations in CarO have been associated with reduced adherence and invasion in tissue culture and with reduced virulence in vivo55. This finding illustrates that defects in the capsule locus are not the only means by which TP1 may gain phage insensitivity. Notably, similar CarO defects were not observed in TP2 or TP3, which attained phage resistance in vivo.

In addition to the common 6bp deletion in the Gtr76 glycosyltransferase and CarO mutation, the other mutations observed in the myophage-insensitive mutants are SNPs or small indels in non-coding regions or that result in missense or silent mutations in a predicted capsular glucose-6-phosphate isomerase Gpi (HWQ22_04190) and an ABC transporter, respectively (Table4). However, these SNPs are not conserved in the in vitro mutants against myophages and were also not detected in in vivo isolates TP2 and TP3, suggesting that the defect observed in the Gtr76 glycosyltransferase is sufficient to confer insensitivity to the cocktail myophages in this strain.

Strain TP1 mutants resistant to the podophage AbTP3phi1 were also found to contain mutations in the capsule locus, but these mutations were confined to the genes encoding the glucose-6-phosphate isomerase Gpi and polysaccharide biosynthesis tyrosine autokinase Wzc (HWQ22_04255) (Table4 and Fig.5A). Loss of function in these genes is expected to result in loss of L-fructose-6-phosphate required for downstream production of capsule monomers and defects in capsule export, respectively32. This suggests that these mutants may exhibit more severe defects in K116 capsule expression, and that AbTP3phi1 requires the presence of the capsule backbone for successful infection.

Our results are consistent with the recently published work by Altamirano et al.34, where a frameshift in the glycosyltransferase and glucose-6-phosphate isomerase within the K locus were detected in two independent phage-resistant A. baumannii mutants. The consistency between our work and that study confirms the A. baumannii capsule locus being important for phage sensitivity. Both Gpi and glycosyltransferases are involved the biosynthesis of capsule K units, which are tightly packed repeating subunits consisting of 4 to 6 sugars56. The reason why one group of phages (our myophages, and the myophage FG02 in ref. 34) selected primarily for defects in the Gtr glycosyltransferase but the other phages (our podophage AbTP3phi1 and myophage CO01 in Altamirano et al.) selected for defects in Gpi is not entirely clear. These phages likely recognize different moieties of the bacterial capsule as their receptors, but it should be noted that many of the mutations associated with insensitivity observed in our study are not necessarily inactivating to the protein: the most common mutation in the capsule locus is a two-residue in-frame deletion in gtr76 (Fig.5B), and the other mutations are single-residue changes or nonsense/frameshift mutations relatively late in the reading frame. These mutations may modulate protein function rather than being inactivating.

Capsule is a known common requirement for A. baumannii phages, and defects in capsule synthesis have been shown to be responsible for phage resistance34,57. The presence of the same 6bp deletion in the capsular glycosyltransferase gene gtr76 of both the in vitro- and the in vivo-selected A. baumannii strains indicates that the same route to phage insensitivity may be followed by strain TP1 in both systems. Importantly, this demonstrates that laboratory in vitro investigations of bacterial selection and phage insensitivity can produce results that are relevant and predictive for the in vivo milieu of clinical treatment.

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Comparative genomics of Acinetobacter baumannii and therapeutic bacteriophages from a patient undergoing phage therapy - Nature.com

Lee S. Schwartzberg, MD, FACP, discusses the utilization of genomics in cancer care.

Lee S. Schwartzberg, MD, FACP, chief of Medical Oncology and Hematology at the Renown Institute for Cancer and professor of Clinical Medicine at the University of Nevada, discusses the utilization of genomics in cancer care.

Myriad is improving care through the development of different tools to examine the combination of genomic profiles of patients with cancer, Schwartzberg says. Tests have been designed to examine genomic alterations, individual genes, broader genomes, and more, which are combined to determine a homologous recombination deficiency (HRD) score, Schwartzberg explains. HRD scores can dictate clinical decisions. This practice has already been used in ovarian cancer, and it is expected to expand to other disease spaces, Schwartzberg adds.

Additional genomic testing, known as genomic profiling, genomic expression, or genomic classifiers, examines the expression of certain genes to create a model that can predict prognosis or response to certain therapies, Schwartzberg explains. For example, a patient with a clinically high-risk tumor could be determined to have a genomically low-risk tumor, and that would dictate treatment decisions, Schwartzberg concludes.

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Dr. Schwartzberg on the Use of Myriad Genomic Testing in Cancer Care - OncLive

Most of the COVID samples sent to National Institute of BioMedical Genomics in Kalyani for genome sequencing tested positive for the BA.5 subvariant of Omicron along with some BA.4

Representational image. PTI

Kolkata: Genome sequencing ofCOVID positive samples has revealed that Omicron subvariants BA.4 and BA.5 have started replacing the BA.2 alternative that caused the surge in cases of the infection in West Bengal earlier this year, a senior official of the state health department said on Thursday.

According to experts, it is mostly subvariant BA.5, which has features identical to BA.2, that is responsible for the recent spike in coronavirus cases in the state.

"We have been conducting genome sequencing on positive samples in West Bengal. A few subvariants of Omicron, mostly the BA.4 and BA.5 was found. But there is nothing to worry about. The BA.5 subvariant though highly infectious is not that threatening, at least for those who have no comorbidities," Siddhartha Niyogi, director of health services said on Thursday.

"Examinations of the samples showed that the subvariant BA.5 is gradually replacing the BA.2," he said.

Most of theCOVID samples sent to National Institute of BioMedical Genomics in Kalyani for genome sequencing tested positive for the BA.5 subvariant of Omicron along with some BA.4.

Kheya Mukherjee, the associate professor of the department of microbiology at Beliaghata ID&BG Hospital held the Omicron subvariant BA.5 responsible for the recent surge in COVID cases in Bengal.

The state, she said, will witness more and more cases in the next few weeks as the infectivity rate of BA.5 is "much more" than its predecessor, the BA.2, she said.

"The steep rise in the number of COVID cases in Bengal is primarily due to this subvariant BA.5. There are cases where subvariant BA.4 is present. There are cases which are still caused by BA.2. I doubt how complicated BA.5 will be compared to BA.2 subvariant because most of the people are vaccinated. The infection will be mild and the fatality rate will be low as well," Mukherjee told PTI.

The microbiologist also predicted that the infection will scale up in the coming days and might reach a peak before receding.

"In the state the contagion has almost doubled in just five days and it shows that there will be several thousands of infections in a day. It may reach a peak at one point of time and then start receding," she said.

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COVID-19: BA.4, BA.5 subvariants cause of spike in West Bengal, say experts - Firstpost

The grant is being used to fund a first-of-its-kind clinical trial that will recruit healthy individuals through a genome-first approach and perform deep metabolic phenotyping to understand the underlying mechanisms responsible for the regulation of the human bodys metabolism through natriuretic peptide hormones.

The grant is being used to fund a first-of-its-kind clinical trial that will recruit healthy individuals through a genome-first approach and perform deep metabolic phenotyping to understand the underlying mechanisms responsible for the regulation of the human bodys metabolism through natriuretic peptide hormones.Researchers from the University of Alabama at Birmingham Division of Cardiovascular Disease have been awarded a $3.7 million grant from the National Heart Lung and Blood Institute to study how genetically determined differences in natriuretic peptide levels (heart hormones) regulate the handling of glucose metabolism and use of energy while resting and while exercising.

The grant is being used to fund a first-of-its-kind clinical trial that will recruit healthy individuals through a genome-first approach and perform deep metabolic phenotyping to understand the underlying mechanisms responsible for the regulation of the bodys metabolism through NPs.

NPs are hormones produced by the heart that regulate cardiometabolic health. These hormones are released in response to changes in pressure inside the heart. These hormones are also responsible for regulating how the body responds to glucose and how it utilizes energy at rest and while working out.

Pankaj Arora, M.D., associate professor of medicine and the director of the $11 million NIH-funded Cardiovascular Clinical and Translational Research Program and the UAB Cardiogenomics Clinic, received the grant.

An estimated 37 million adults in the United States have diabetes, and an additional 96 million adults have pre-diabetes, which predisposes them to a higher risk of potentially fatal cardiovascular events such as heart attack, stroke and heart failure.

Researchers believe that genetically determined low NP levels may contribute to some individuals having a poor glucose metabolism and a low amount of any exercise. Individuals with lower circulating NP levels are predisposed to a higher risk of cardiometabolic diseases such as diabetes, high blood pressure, heart attacks, stroke and heart failure.

Pankaj Arora, M.D., associate professor of medicine and the director of the $11 million NIH-funded Cardiovascular Clinical and Translational Research Program and the UAB Cardiogenomics Clinic, received the grant.The study is employing an innovative genome-first strategy to assess the role of NPs in regulating the cardiovascular and metabolic health of an individual, Arora said. We will be enrolling individuals with and without a common genetic variant that predisposes them to have low NP levels. The study participants will then undergo a comprehensive metabolic assessment to understand the influence of genetically determined low NP levels.

The study is the result of decades of interdisciplinary research conducted by UAB scientists in collaboration with investigators across the country. Through past research, Arora and colleagues have shown that certain RNA-based regulators control the production of NPs and serve as potential therapeutic targets. Arora and his colleagues are studying how these regulators can be targeted for a precision medicine approach to the treatment of common cardiometabolic diseases.

There are certain RNA-based regulators that control the production of these good heart hormones that were discovered by our group of researchers, Arora said. These regulators reduce the production of NPs in individuals with a low NP genotype and may serve as potential therapeutic targets for the treatment of high blood pressure, diabetes, pre-diabetes and heart failure.

In addition to an innovative genome-first approach, the study by Arora and colleagues may also unravel a potentially new line of personalized therapeutics that follow the same genome-first precision medicine approach.

Arora believes that innovative studies like these build upon the advances in genomic medicine and bring the knowledge of decades of research back to the benefit of the patients at their bedside. UAB has been supporting such bench-to-bedside initiatives that translate scientific evidence accumulated from large-scale population genomic studies and bench research to the patient bedside. UAB physician-scientists are leading several such initiatives to enhance clinical and translational research in the domains of cardiometabolic disease.

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Arora receives $3.7 million grant to assess a genome-first approach to improving cardiometabolic health through heart hormo - University of Alabama at...

Shapiros Nature Ecology study also focused on what may have happened to other polar bear genomes during periods of low icein this case, around 120,000 or 125,000 years ago when, according to Shapiro, Arctic ice levels were similar to the present days. But here, she looked at the relationship between polar bears and brown bears.

Her team constructed a phylogenetic treesort of like an evolutionary map showing how the bears diverged from a common ancestor over timeusing Brunos genome and those of currently living polar bears, brown bears, and a black bear. (Shapiro was able to utilize one of Laidres Southeast Greenland polar bear genomes in her analyses, although the time gap between its life and Brunos is enormous. The sample pool, she says, is missing 100,000 years of evolution.)

From this and other analyses, the scientists gained some evidence that about 20,000 years before Bruno was born, brown bears and polar bears mixed to generate hybrid offspring. The scientists hypothesized that during this warm period, polar bears might have made their way on shore. The carcasses of the marine mammals they hunted could have attracted brown bearsleading to mating opportunities. As a potential result of this ancient interbreeding, Shapiro says, up to 10 percent of the genome of the modern brown bear comes from polar bear ancestry.

Figuring out how and when polar bears and brown bears commingled, further specialized, or diverged is a difficult task, given the limited fossil record and complexities of evolution. Evolution is a messy process, says Andrew Derocher, a polar bear researcher at the University of Alberta who was unaffiliated with the studies. He likens the process of evolutionary speciation to a massive bunch of vines that are creeping up the base of a tree, crisscrossing and entangling. Eventually, some of those vines might get their own trajectory, and thats what our species are, he says. But in this process, they can cross over, they can reconnect and fuse, and its certainly impossible to pull it apart, because theyre so interconnected.

Still, these two studies are linked, Laidre says, in the sense of: Where have polar bears persisted when sea ice was low, and how? The research may provide some insight into how bears in the pastand todays Southeast Greenland bearshave survived in warmer climates with less ice.

But how genetic changes manifest in physical form, and how those changes may have helped bears survive past warming events, are still open questions, the scientists say. And these study results shouldnt make us feel that the problem of Arctic warming is resolved, or that todays bears can easily adapt to rapidly shrinking levels of sea ice. It seems like global warming is happening too fast, Lindqvist says. She wonders if the polar bears can keep up.

After all, polar bears depend on seals as their food sourceand those seals depend on sea ice. Theres parts of the Arctic that used to be excellent seal habitats and excellent polar bear habitats, Derocher says. But theres no sea ice there anymore. And as a result, theres virtually no bears. Theres very few seals, and the ecosystem has basically unraveled.

What, then, might actually help? Global action on climate change, Laidre says. Thats it.

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What Polar Bear Genomes May Reveal About Life in a Low-Ice Arctic - WIRED

Different structures were detected between the Brazilian and US genetic bases

Principal component analysis (PCA) revealed that most Brazilian cultivars (red circle) were grouped with a subgroup of US cultivars (green circle). Most of them belonged to MG VI, VII, VIII and IX (Fig.1A). Based on the Evanno criterion (Fig.1B), the structure results based on four groups (K=4) showed a high K value (312.35), but the upper-most level of the structure was in two groups (K=2; K=1885.43).

Population structure analysis between Brazilian and US germplasms. (A) Principal component analysis of Brazilian and US soybean cultivars based on SNPs markers; (B) Delta K as a function of the number of groups (K); (C) assignment coefficients of individual cultivars (bar plots) considering K=2; and (D) considering K=4.

Considering K=2 (Fig.1C), the Brazilian cultivars jointly presented an assignment to the Q1 group (green) equal to 86.7% which was much higher than that observed for the US cultivars (43.9%). Considering K=4 (Fig.1D), the Brazilian cultivars jointly presented an assignment to the Q2 group (red) of only 4.7% while the US cultivars jointly presented an assignment to the Q2 group of 27.4%. The Q1 group (green) has a lower assignment in Brazilian cultivars than US accessions (11.1%, and 30.1%, respectively). These results demonstrate that the set of Brazilian cultivars has a narrower genetic base compared to US cultivars.

When we compared the cultivars between maturity groups, we observed a clear differentiation between early and late groups. The highest genetic distances (0.4158) observed were between MG 000 and MG VIII-IX cultivars (Supplementary Table S1).

To examine the influence of maturity groups on population structure, we analyzed the average assignment coefficients (K=4) of Brazilian and US cultivars for each maturity group (Supplementary Figure S1). Brazilian cultivars from maturity group V presented Q1, Q2, Q3, and Q4 equal to 30.4%, 1.9%, 32.1, and 32.0%, respectively; US cultivars from this same maturity group (V) presented means of Q1, Q2, Q3, and Q4 equal to 9.2%, 8.2%, 65.1%, and 17.6%, respectively. This result indicates that, although belonging to the same maturity group, the Brazilian group V cultivars present considerably different allelic frequencies than the US cultivar group V cultivars, especially for Q3 and Q4. US cultivars belonging to earlier maturity groups (00, 0, I, and II) had significantly higher mean assignment coefficient to Q2 group (red) compared to other later maturity groups (V=8.2%, VI=8.1%, VIII=5.0%, and IX=13.6%). In the case of Brazilian cultivars, the average assignment coefficients for Q2 were much lower (V=1.9%, VI=4.2%, VII=5.6%, VIII=4.9% and IX=4.9%). These results demonstrate an important allelic pool that distinguishes early to late genetic materials present in Q2.

In general, the Brazilian germplasm showed few differences between maturity groups (Supplementary Table S1 and Fig.2A). This was also observed when we generated a population structure analysis exclusively with these cultivars (Fig.2C). In contrast, the US germplasm showed a high variation of genetic distance when we analyzed their maturity groups (Supplementary Table S1) with a clear clustering of cultivars (Fig.2B), which is more obvious when we observed their exclusive population structure analysis (Fig.2D). The results show that early cultivars tend to be genetically distant from late cultivars in the US. The maturity groups from the southern-breeding program of the US (V, VI, VII, VIII, and IX) tend to be less genetically divergent versus northern groups (00, 0, I, II, III, and IV). This agrees with previous studies indicating distinct Northern and Southern genetic pools in the US6. There is a low divergence among US soybean cultivars from maturity groups higher than V (Fig.2B). In contrast, cultivars from MG 00 and 0 were more genetically distant from cultivars of MG III and IV while maturity groups I-II were an intermediate group. The population structure analysis showed a high influence of Q2 in cultivars with MG 00-II. For cultivars in MG III and IV, we observed an increase of Q1. Finally, there is a high influence of Q3 in cultivars with maturity groups higher than V, which agrees with the genetic distance data.

Population structure analysis of Brazilian and US cultivars according to their maturity groups. Principal component analysis (PCA) within Brazilian (A) and US (B) germplasms for each maturity groups; population structure of the Brazilian (C) and the US (D) genetic basis arranged according to their maturity groups.

The results demonstrate that both genetic bases had few increases in genetic distance among modern genetic materials (releases after 2000) when compared to cultivars from the 1950s to 1970s (Supplementary Table S2). According to the IBS genetic distance mean, the Brazilian genetic base was more diverse over the decades compared to US germplasm especially when we compared cultivars released before the 1970s and released after the 2000s (Supplementary Table S2).

Average assignment coefficients (Q1, Q2, Q3, and Q4) from genetic structure results were calculated for both germplasm pools. All accessions were sorted according to their origin and decade of release (Fig.3). We observed high genomic modifications over the decades in the Brazilian germplasm. Modern genetic materials (20002010) had Q1, Q2, Q3, and Q4 values of 36.8%, 2.3%, 31.7%, and 26.0%, respectively, while old accessions (1950-1960s) had means of Q1, Q2, Q3, and Q4 equal to 1.6%, 6.6%, 7.0%, and 84.7%, respectively. A high decrease was observed for Q4 starting in the 1990s whereas Q1 and Q3 highly increased during the same period. For the US genetic base, we observed an increase of Q3 and a decrease of Q2 over time. Old cultivars (19501970) had Q1, Q2, Q3, and Q4 values of 36.0%, 33.7%, 12.3%, and 18.1%, respectively, while modern cultivars (20002010) had Q1, Q2, Q3, and Q4 of 24.3%, 17.5%, 40.3%, and 17.8%, respectively.

Mean assignment coefficients of the Brazilian and US cultivars belonging to the different decades of release (1950 to 2010) to STRUCTURE groups (Q1, Q2, Q3, and Q4) considering K=4.

Modification during the 1990s became more evident upon analysis of the PCA and genetic structure results of the Brazilian genetic base considering the decades of release (Fig.4A and C). We observed an increase in the influence of the Q2 in modern genetic materials (20002010) when we compared the results to old genetic materials (19501970). In contrast, the US genetic base showed few variations over time according to the average of genetic distance (Supplementary Table S2), PCA, and the exclusive population structure analysis (Fig.4B and D). These results suggest a large influence of new alleles in the Brazilian germplasm after the 1990s.

Population structure of Brazilian and US cultivars according to their decade of release. Principal component analysis (PCA) within Brazilian (A) and US (B) germplasm for each decade; population structure of the Brazilian (C) and the US (D) genetic bases arranged according to their decade of release.

Seventy-two SNPs with FST0.4 between Brazilian and US cultivars were identified (Supplementary Table S3). These SNPs are located on chromosomes 1, 4, 6, 7, 9, 10, 12, 16, 18, and 19 (Supplementary Figure S2). Twenty-six 100-Kbp genomic regions with a high degree of diversification between Brazilian and US genetic bases were also found (Table 1). The results for Tajimas D showed that these regions had balancing events that maintained the diversity of their bases. Two regions on chromosome 6 (47.3 47.4 Mbp and 47.347.4 Mbp) and another on chromosome 16 (31.1031.20 Mbp) had few variations in Brazilian accessions (Supplementary Table S4). In contrast, the allele distribution for most of the SNPs present in these genomic regions in US germplasm was higher compared to Brazilian germplasm. An opposite scenario was observed for the other three regions located on chromosomes 7 (6.30 6.40 Mbp), 16 (30.70 30.80), and 19 (3.00 3.10) (Supplementary Table S4). The allele variance was higher in the Brazilian genetic base than US germplasm for these three intervals.

Six SNPs located close to maturity loci E1 (Chr06: 20,207,077 to 20,207,940bp)14, E2 (Chr10: 45,294,735 to 45,316,121bp)15, and FT2a (Chr16: 31,109,999 to 31,114,963)16 had a large influence on the differentiation of the Brazilian and US genetic bases (Fig.5). For the SNPs ss715607350 (Chr10: 44,224,500), ss715607351 (Chr10: 44,231,253), and ss715624321 (Chr16: 30,708,368), we found that the alternative allele was barely present in US germplasm whereas the Brazilian genetic base had an equal distribution between reference and alternative alleles. When we examined the SNPs ss715624371 (Chr16: 31,134,540) and ss715624379 (Chr16: 31,181,902), the frequency of the alternative allele remains low in the US germplasm. However, the alternative alleles of these two SNPs were present in more than 78% of the Brazilian accessions in contrast to the previous three SNPs. Finally, the alternative allele for SNPs ss715593836 (Chr06: 20,019,602) and ss715593843 (Chr06: 20,353,073) were extremely rare in Brazilian germplasm with only 2% of the accessions carrying them. In contrast, the US germplasm had an equal distribution of reference and alternative alleles in their accessions. However, all accessions with the alternative alleles belonged to MGs lower than VI with less than five cultivars in MG V.

The allele frequency distribution for SNPs close to loci (A) E1 (chromosome 6), (B) E2 (chromosome 10), and (C) FT2a (chromosome 16) in Brazilian and US germplasms.

Ten SNPs were identified related to the genes modifier mutations present in Brazilian and US germplasm; these were distributed on chromosomes 4, 6, 10, 12, 16, and 19 (Supplementary Table S5). These SNPs had differing allele frequencies and could distinguish both genetic bases. Six modifications had a clear influence on the maturity of the accessions whereas two of these had a large influence in some decades of breeding (Supplementary Figure S3). The SNP ss715593833 had a similar haplotype as two SNPs described as close to the E1 loci (ss715593836 and ss715593843) due to the linkage disequilibrium (LD) among them. At the end of this chromosome, we also observed another three relevant SNPs in LD: ss715594746, ss715594787, and ss715594990. In the US germplasm, we observed a decrease in the alternative allele in accessions with MG values lower than IV. We detected other relevant modifications on chromosome 12 for SNPs ss715613204 and ss715613207. Both SNPs had a minor allele frequency higher than 0.35 in Brazilian germplasm with an increase in the alternative allele in cultivars with MGs higher than VII. In contrast, alternative alleles for both SNPs were extremely rare in the US germplasm except for accessions with MG higher than VII.

There were 312 genomic regions that differentiate northern (00 IV MG) and southern (V IX MG) cultivar groups (Supplementary Table S6), which included the Dt1 locus. We compared the SNPs observed in the genomic region close to the Dt1 gene (Chr19: 45.2045.30 Mbp) with the growth habit phenotype data available for 284 lines at the USDA website (www.ars-grin.gov). The phenotypic data suggests that these SNPs are associated with growth habit. Moreover, our diversity analysis demonstrated a putative selective sweep for the Dt1 gene in the northern germplasm, which has the dominant loci fixed for Dt1; the southern lines tend to be more diverse compared to the northern US cultivars (Supplementary Table S7). In contrast, other genomic regions have lower nucleotide diversity in southern accessions compared to the northern accessions. An important disease resistance gene cluster was observed on chromosome 13 bearing four loci: Rsv1, Rpv1, Rpg1, and Rps317,18,19,20. In this interval, we observed two genomic regions (29.70 29.80 Mbp and 31.90 32.00 Mbp) under putative selective sweeps in the southern germplasm (Supplementary Table S8).

Besides these regions, 1,401 SNPs with FST values higher than 0.40 between northern and southern US cultivars were also identified (Supplementary Table S9). In addition, there were 23 SNPs with FST values higher than 0.70 spread on chromosomes 1, 3, 6, and 19. Seven of them were located close to another important soybean locus: E1 (involved in soybean maturity control) (Supplementary Table S10). These SNPs clearly differentiate northern and southern US cultivars with the reference allele fixed in northern genetic materials, and the alternative alleles in southern accessions. Gene modification in US germplasm was also detected in our study. One hundred twenty-six SNPs were identified in FST analysis modifying 125 genes (Supplementary Table S11).

Finally, we detected 1,557 SNPs with FST values higher than 0.40 between super-early cultivars (00 0 MG) and early cultivars (III IV MG) (Supplementary Table S12). Seventeen SNPs had FST values higher than 0.70 spread on chromosomes 4, 7, 8, and 10. The SNPs identified on chromosome 10 were close to the E2 locus. We also detected 168 SNPs associated with modifications in 164 genes (Supplementary Table S13).

We observed two SNPs with large differences in allelic frequencies in the Brazilian germplasm (Supplementary Figure S4). On chromosome 4, SNP ss715588874 (50,545,890bp) had a decrease of the allele A in cultivars released after 2000 with only nine of the 45 Brazilian cultivars with this allele. A similar situation was observed on chromosome 19 for ss715633722 (3,180,152bp) with half of the modern accessions having the presence of allele C. Both SNPs had similar distribution according to their decades in the US genetic base with a large influence of reference alleles.

There were 126 genomic regions spread on almost all soybean chromosomes in Brazilian cultivars. The only exception was chromosome 20 (Supplementary Table S14). Our analysis between cultivars released before and after 1996 identified 30 putative regions under breeding sweep events. Thirteen regions had a decrease in diversity in modern genetic cultivars according to Tajimas D and results. Two genomic regions observed were close to important disease resistance loci: one on chromosome 13 (30.30 30.40 Mbp) close to the resistance gene cluster (with Rsv1, Rpv1, Rpg1, and Rps3)17,18,19,20 and another on chromosome 14 (1.70 1.80 Mbp) with a southern stem canker resistance loci21,22. In contrast, thirty-one genomic regions had an increase in diversity in modern cultivars, which suggested putative introgression events in these accessions. Two genomic regions were observed, on chromosome 2 (40.90 40.10 Mbp) and 9 (40.3040.40 Mbp). Thesewere previously reported to have an association with ureide content and iron nutrient content, respectively23,24.

Besides these regions, there were also 409 SNPs with FST values higher than 0.40, distributed across all soybean chromosomes. There were 73 SNPs with FST values higher than 0.70 (Supplementary Table S15). Some of these SNPs were also reported to be associated with important soybean traits such as plant height, seed mass, water use efficiency, nutrient content, and ureide content23,24,25,26,27.

We also identified gene modifications with a high impact on the Brazilian genetic base when we compared cultivars according to their decade of release. Of the 409 SNPs identified in FST analysis, we observed 40 SNPs causing modifications in 39 soybean genes (Supplementary Table S16). Three SNPs with FST values higher than 0.70 were associated with non-synonymous modifications: ss715588896 (Glyma.04G239600 a snoaL-like polyketide cyclase), ss715607653 (Glyma.10g051900 a gene with a methyltransferase domain), and ss715632020 (Glyma.18G256700 a PQQ enzyme repeat).

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Genetic relationships and genome selection signatures between soybean cultivars from Brazil and United States after decades of breeding | Scientific...

NEW YORK An analysis of monkeypox virus (MPXV) genomes from the ongoing global outbreak has found that the samples cluster together, indicating a single origin for them.

Between the beginning of the year and the middle of June, there have been more than 2,100 laboratory-confirmed cases of monkeypox, most of which have been reported since the start of May, according to the World Health Organization. More than 80 percent of these cases have been reported in Europe and 12 percent in the Americas, where the virus is not endemic and the cases have no known links to endemic regions.

Researchers in Portugal where there have been about 300 cases, according to the European Centre for Disease Prevention and Control have now conducted a phylogenetic analysis of 2022 MPXV and found that the outbreak likely has a single source related to a 2017/2018 outbreak in Nigeria. They additionally reported in Nature Medicine on Friday that the virus samples appeared to be undergoing accelerated evolution, likely influenced by host APOBEC3, a class of mRNA-editing enzymes that help defend against viruses.

"The accelerated evolution is an observation, but we do not know yet how that happened. It was quite unexpected to find so many mutations in the 2022 MPXV," senior author Joo Paulo Gomes from the National Institute of Health Doutor Ricardo Jorge in Lisbon said in an email.

He and his colleagues analyzed the first 2022 MPXV genome from the outbreak, which they released publicly on May 19, in conjunction with 14 other MPXVgenome sequences, most of which were also from Portugal.

A phylogenetic analysis placed the 2022 outbreak samples among clade 3, within what was formerly known as the "West African" clade. All the outbreak samples clustered tightly together, indicating a single origin for the ongoing outbreak.

At the same time, the outbreak samples formed a branch that diverges from viruses linked to cases in the UK, Israel, and Singapore in 2018 and 2019, which themselves stemmed from an outbreak in Nigeria from 2017/2018. This suggested to the researchers that the 2022 outbreak could be due to the continuous circulation and evolution of the virus from the Nigeria outbreak.

However, 2022 MPXV differs from the 2018/2019 virus by an average 50 SNPs, which Gomes noted is many more than expected. For this type of virus, he said, one or two mutations would be expected to arise each year. As 2022 MPXV is likely a descendant of the 2017/2018 Nigeria outbreak which led to the UK, Israel, and Singapore cases in 2018/2019 about 5 to 10 additional mutations would be expected, not 50.

"So, unquestionably, we are facing a scenario of accelerated evolution," Gomes said.

The changes also tended to follow a certain pattern of incorporating more adenines and thymines into an already A/T-rich viral genome, which suggested that the human APOBEC3 system could be involved in this accelerated evolution.

APOBEC3 is a host antiviral mechanism that induces mutations into viruses, but that could lead to hypermutation if the enzymes do not fully restrict the viruses. Gomes noted that this mechanism has already been described in HIV and HPV.

"We do not know about the consequences but we know, for instance, that [a number] of these mutations are affecting viral proteins that are associated with the interaction with the human immune system, so, hypothetically, a mechanism of immune evasion cannot be completely discarded," he added.

In all, the researchers said that viral genome sequencing of outbreak samples may enable scientists to better understand how 2022 MPXV is spreading and provide insight into ways to control that spread. "We will focus on identifying and monitoring the mutations that will arise in real time during the ongoing transmission in order to better understand the host adaptation," Gomes said.

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Monkeypox Genome Analysis Points to Single Origin of Recent Outbreak - GenomeWeb