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This article was originally published here

Mol Biotechnol. 2022 Jan 29. doi: 10.1007/s12033-021-00431-7. Online ahead of print.

ABSTRACT

Biotechnological approaches have always sought to utilize novel and efficient methods in the prevention, diagnosis, and treatment of diseases. This science has consistently tried to revolutionize medical science by employing state-of-the-art technologies in genomic and proteomic engineering. CRISPR-Cas system is one of the emerging techniques in the field of biotechnology. To date, the CRISPR-Cas system has been extensively applied in gene editing, targeting genomic sequences for diagnosis, treatment of diseases through genomic manipulation, and in creating animal models for preclinical researches. With the emergence of the COVID-19 pandemic in 2019, there is need for the development and modification of novel tools such as the CRISPR-Cas system for use in diagnostic emergencies. This system can compete with other existing biotechnological methods in accuracy, precision, and wide performance that could guarantee its future in these conditions. In this article, we review the various platforms of the CRISPR-Cas system meant for SARS-CoV-2 diagnosis, anti-viral therapeutic procedures, producing animal models for preclinical studies, and genome-wide screening studies toward drug and vaccine development.

PMID:35091986 | DOI:10.1007/s12033-021-00431-7

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The Trend of CRISPR-Based Technologies in COVID-19 Disease: Beyond Genome Editing - DocWire News

[93 Pages Report] Digital Genome Market Insights 2022 This report contains market size and forecasts of Digital Genome in United States, including the following market information:

United States Digital Genome Market Revenue, 2016-2021, 2022-2027, (USD millions)

United States top five Digital Genome companies in 2020 (%)

The global Digital Genome market size is expected to growth from USD 6963.3 million in 2020 to USD 10930 million by 2027; it is expected to grow at a CAGR of 6.2% during 2021-2027.

The United States Digital Genome market was valued at USD million in 2020 and is projected to reach USD million by 2027, at a CAGR of % during the forecast period.

The Research has surveyed the Digital Genome Companies and industry experts on this industry, involving the revenue, demand, product type, recent developments and plans, industry trends, drivers, challenges, obstacles, and potential risks.

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Leading key players of Digital Genome Market are

Digital Genome Market Type Segment Analysis (Market size available for years 2022-2027, Consumption Volume, Average Price, Revenue, Market Share and Trend 2015-2027): Sequencing Services, Sequencing Instruments, Sequencing Consumables, Bioinformatics, Sample Preparation Kits and Reagents

Regions that are expected to dominate the Digital Genome market are North America, Europe, Asia-Pacific, South America, Middle East and Africa and others

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(New Report) Digital Genome Market In 2022 : The Increasing use in Diagnostics, Agriculture & Animal Research, Personalized Medicine, Drug...

Significance

This is a significant sister group contrast comparative study of the underpinning genomics and evolution of desiccation tolerance (DT), a critical trait in the evolution of land plants. Our results revealed that the DT grass Sporobolus stapfianus is transcriptionally primed to tolerate a dehydration/desiccation event and that the desiccation response in the DT S. stapfianus is distinct from the water stress response of the desiccation-sensitive Sporobolus pyramidalis. Our results also show that the desiccation response is largely unique, indicating a recent evolution of this trait within the angiosperms, and that inhibition of senescence during dehydration is likely critical in rendering a plant desiccation tolerant.

Desiccation tolerance is an ancient and complex trait that spans all major lineages of life on earth. Although important in the evolution of land plants, the mechanisms that underlay this complex trait are poorly understood, especially for vegetative desiccation tolerance (VDT). The lack of suitable closely related plant models that offer a direct contrast between desiccation tolerance and sensitivity has hampered progress. We have assembled high-quality genomes for two closely related grasses, the desiccation-tolerant Sporobolus stapfianus and the desiccation-sensitive Sporobolus pyramidalis. Both species are complex polyploids; S. stapfianus is primarily tetraploid, and S. pyramidalis is primarily hexaploid. S. pyramidalis undergoes a major transcriptome remodeling event during initial exposure to dehydration, while S. stapfianus has a muted early response, with peak remodeling during the transition between 1.5 and 1.0 grams of water (gH2O) g1 dry weight (dw). Functionally, the dehydration transcriptome of S. stapfianus is unrelated to that for S. pyramidalis. A comparative analysis of the transcriptomes of the hydrated controls for each species indicated that S. stapfianus is transcriptionally primed for desiccation. Cross-species comparative analyses indicated that VDT likely evolved from reprogramming of desiccation tolerance mechanisms that evolved in seeds and that the tolerance mechanism of S. stapfianus represents a recent evolution for VDT within the Chloridoideae. Orthogroup analyses of the significantly differentially abundant transcripts reconfirmed our present understanding of the response to dehydration, including the lack of an induction of senescence in resurrection angiosperms. The data also suggest that failure to maintain protein structure during dehydration is likely critical in rendering a plant desiccation sensitive.

Desiccation tolerance (DT) is a fundamental trait that is widespread and developed early in the evolution of the land plants (1, 2), and it is believed to have been critical in the colonization of the land by green algae (3). In tracheophytes, DT is generally limited to reproductive propagules, such as seeds and spores, while vegetative desiccation tolerance (VDT) occurs in only 0.086% of known vascular plant species (4). Our understanding of VDT (and its relationship to seed DT) has broadened with the recent expansion of whole-genome sequencing of resurrection plants, tracheophytes that can survive the desiccation of their vegetative tissues. Since the release of the Boea hygrometrica genome sequence (5), the genomes of four other resurrection angiosperms [Xerophyta schlecteri (6), Oropetium thomaeum (7, 8), Lindernia brevidens (9), and Eragrostis nindensis (10)], two lycophytes [Selaginella tamariscina (11) and Selaginella lepidophylla (12)], and the bryophyte Syntrichia caninervis (13) have been published. Apart from the obvious benefits of obtaining genomic resources for individual resurrection species, the establishment of a collection of resurrection plant genomes offered the possibility of the reconstruction of an ancestral genome of a desiccation-tolerant progenitor that would reveal a genomic signature (blueprint) that defines a common mechanism for DT. However, a genomic blueprint for DT has not emerged (4), which may be related to the small number of genomes available and limited phylogenetic sampling, that all tracheophytes possess desiccation-tolerant propagules (seeds or spores), which would obfuscate the comparative analyses, or that the origin of DT lies deep in the land plant phylogeny and is thus cryptic in the recent plant lineages. It may also be a combination of these possibilities or that there is no genomic blueprint for this fundamental trait. Although a genomic blueprint for DT has not been revealed, comparative studies have demonstrated that certain gene families, such as those for early light-inducible proteins (ELIPs) and late embryogenesis-abundant proteins, have expanded in species that exhibit VDT (6, 14, 15).

A corollary to the ancestral reconstruction approach to understanding the evolution of VDT and the genomic aspect of its phenotypic expression is the comparison of the genomes of closely related species that contrast the two extremes: sensitivity and tolerance. Such closely related contrasting species pairings are rare in resurrection plants, but this approach has been applied, albeit with species pairs that are not as close as would be ideal. The genomes and dehydrationrehydration transcriptomes of two resurrection eudicots within the Linderniaceae family (16), the desiccation-tolerant L. brevidens and the desiccation-sensitive (DS) Lindernia subracemosa, were sequenced and compared (9). The comparison revealed that at least in the Lindernia lineage, VDT evolved via a combination of gene duplications in gene families that are functionally associated with the desiccation response and a network-level rewiring of gene expression in vegetative tissue commonly associated with seed desiccation. More recently, a comparative analysis of two contrasting grass genomes along with their respective desiccation-related transcriptomes, the desiccation-tolerant E. nindensis and the related DS cereal Eragrostis tef, reinforced the potential role of gene duplications in the evolution of DT (10). Although there is still a significant phylogenetic distance between these two Eragrostis species (17), the comparative analysis and its extension to include other C4 grasses, including the desiccation-tolerant O. thomaeum, revealed chromatin restructuring and methylation patterns associated with down-regulated genes and specific seed-related orthologs whose expression is associated with VDT. The comparative transcriptome analyses indicated that genes having important roles in seed development and DT are broadly expressed under dehydration in both sensitive and tolerant species, with just a few genes uniquely expressed in the tolerant plants.

In this study, we have chosen two phylogenetically closely related C4 grasses, the homoiochlorophyllous desiccation-tolerant Sporobolus stapfianus and the DS Sporobolus pyramidalis, to develop detailed comparative genomic and transcriptomic analyses to further explore genomic inferences into the evolution of VDT. S. stapfianus and S. pyramidalis are members of the same clade, clade A, in the Sporobolus family of the Sporobolinae subtribe of the Chloridoid grasses (18). S. stapfianus has been the subject of many mechanistic studies of its DT phenotype (19, 20) and along with S. pyramidalis, the subject of a detailed comparative leaf metabolomics study that highlighted differences in the metabolic responses of the two species to dehydration (21). We constructed Hi-Cderived assemblies of the sequenced genomes for both species and conducted transcript profiling analyses for parallel reductions in water contents for both species as well as a full desiccation drying series for S. stapfianus. We performed a detailed comparative genomic analysis for the two species and extended the analysis to include other grass species, both desiccation tolerant and DS. Our results offer insights into the mechanism and evolution of VDT in the Chloridoid grasses.

One-step flow cytometric assays generated size estimates for each of the Sporobolus genomes. The haploid genome of S. stapfianus had an average of 1,385 pg of DNA per nucleus, which is approximately equal to a complete genome sequence of 1.354 Gbp, and the haploid genome of S. pyramidalis had an average of 1,867 pg of DNA per nucleus, which is 1.826 Gbp (Table 1). Draft genome assemblies were generated for each grass using Illumina whole-genome shotgun sequencing combined with Chicago and Hi-C proximity ligation (Materials and Methods). The final assemblies consisted of 11,574 scaffolds with an N50 of 19.4 Mb for S. stapfianus and 2,518 scaffolds with an N50 of 21.6 Mb for S. pyramidalis, with the longest scaffolds for both species greater than 60 Mbp. Despite their high contiguity, the assembled genomes are smaller than the estimated genome size, at 1.080 and 1.055 Gbp for S. stapfianus and S. pyramidalis, respectively. These differences between the estimated and assembled genome sizes are likely caused by collapsed homologous regions in these complex polyploid species as described in detail below. Both genomes have similar levels of repetitive elements, 39.7 and 41.3% for S. stapfianus and S. pyramidalis, respectively (Table 2), with almost identical distributions of known repeat families (SI Appendix, Table S1). Gypsy and Copia retrotransposons are the most predominant families of the known repeats at 36 and 10 to 12%, respectively, for the two genomes.

Estimation of the genome size (1C value) using flow cytometry

S. pyramidalis and S. stapfianus genome assemblies

The Sporobolus genomes were annotated using MAKER with a combination of RNASeq and PacBio Iso-Seq full-length transcripts as expressed sequence tag (EST) evidence and protein homology from other high-quality plant genomes. After filtering, the final annotations contained 52,208 and 51,207 gene models for S. stapfianus and S. pyramidalis, respectively (Table 2). Annotation completeness was assessed using Benchmarking Universal Single-Copy Orthologs (BUSCO) with the poales_odb10.201911-20 database of 4,896 conserved genes. The genome annotations recovered 93.5 and 92.4% of complete BUSCOs for S. stapfianus and S. pyramidalis, respectively, indicating that both genomes were well annotated and contained the vast majority of the coding portion of these two genomes (Table 3). Gene models were functionally annotated using a simplified maizeGAMER pipeline; 96% of genes were annotated with InterProScan domain/family information, and 66% were annotated with Gene Ontology (GO) descriptions for both genomes.

Genome assemblies BUSCO v4 statistics vs. the grass (poales_odb10) dataset

Sporobolus belongs to the Chloridoideae subfamily of grasses, a large and diverse group of predominantly C4 species with remarkable drought, heat, and salinity tolerance. The orphan grain crops finger millet and teff are found within Chloridoideae, as are several model desiccation-tolerant plants in the genera Oropetium, Eragrostis, Tripogon, and Sporobolus among others. Most of the surveyed Chloridoideae species (90%) are polyploid, including species from many of the aforementioned taxa. The availability of several high-quality chloridoid genomes facilitates detailed comparative genomic comparisons within these grasses. Macrosynteny between S. stapfianus and S. pyramidalis shows a clear 2:3 pattern, consistent with the tetraploid and hexaploid nature of these grasses, respectively (Fig. 1 and SI Appendix, Fig. S1). Comparisons with the closely related diploid chloridoid grass O. thomaeum also revealed 1:2 and 1:3 patterns of synteny for S. stapfianus and S. pyramidalis, respectively, supporting their polyploidy (Fig. 1 and SI Appendix, Fig. S2). Although neither Sporobolus genome is scaffolded into complete chromosomes, large 20-Mb+-sized scaffolds are highly collinear with the Oropetium genome with few structural large-scale rearrangements (SI Appendix, Fig. S2), which is consistent with the unusually high conservation of karyotype and collinearity observed among other chloridoid grass genomes (22).

Microsynteny within Chloridoideae grasses. A collinear region between O. thomaeum, S. stapfianus, and S. pyramidalis is highlighted, reflecting the ploidy of each species (diploid, tetraploid, and hexaploidy, respectively). Genes are shown in blue and green, and syntenic gene pairs are connected by gray lines.

Macrosyntenic analysis between the Sporobolus species and O. thomaeum exposed an overall more complex polyploid structure than the more straightforward tetraploid and hexaploid compositions (SI Appendix, Fig. S2). Roughly half the hexaploid S. pyramidalis genome has the expected 3:1 pattern of syntenic blocks compared with O. thomaeum, while 37% is only 2:1. The pattern is similar for tetraploid S. stapfianus, where 44% of syntenic blocks are 2:1 to O. thomaeum as expected and 42% of blocks are 1:1 (SI Appendix, Fig. S2). Similar assembly issues were observed in the tetraploid chloridoid grass E. nindensis, where one to four regions were assembled for each syntenic region in O. thomaeum (10). These discrepancies, combined with differences between the estimated and assembled genome sizes, suggest the Sporobolus genomes were partially collapsed during assembly in homologous regions. S. pyramidalis and S. stapfianus may be segmental allopolyploids with varying degrees of homology between chromosomes from separate subgenomes. Partial collapse during assembly would result in divergent homologous regions assembling separately and highly similar regions collapsing, which is supported by the observation that the ratio of assembled syntenic blocks is maintained across large syntenic blocks and whole chromosomes in O. thomaeum. For instance, two homologous regions are assembled in S. pyramidalis for chromosomes 3 and 4 from O. thomaeum, while three regions in S. pyramidalis were identified for most of chromosome 2 in O. thomaeum. Similar patterns were observed between S. stapfianus and O. thomaeum. To account for these issues related to polyploidy, syntenic gene pairs and orthogroups were used for downstream comparative genomics and transcriptomics analyses between the Sporobolus genomes and other chloridoid grasses.

We generated RNASeq data from RNA isolated from leaf tissues at different stages of dehydration for both species (SI Appendix, Fig. S3). Differentially expressed genes were identified using edgeR (23), and the resulting gene lists were assigned to GO biological process categories enrichment using the Cytoscape (23) plugin Bingo (24). These analyses indicate that S. pyramidalis and S. stapfianus transcriptomes respond differently to dehydration and share few biological process adaptations during the drying process. When water content decreases from 3 to 2 grams of water (gH2O) g1 dry weight (dw), S. pyramidalis exhibits a strong response with 11,978 statistically differentially abundant transcripts (SDATs), in contrast to the more moderate response of 1,776 SDATs in S. stapfianus (Fig. 2 A and B). A GO enrichment analysis of SDAT lists further demonstrates that during the 3 to 2 gH2O g1 dw water content transition, few biological processes are shared between the two species (Fig. 2 C and D and SI Appendix, Fig. S4). Some biological process categories, including response to heat and response to reactive oxygen species, are common to both species (SI Appendix, Fig. S4). Moreover, while S. pyramidalis responds to the change in water content from 3 to 2 gH2O g1 dw by modulating processes involving the ribosome and the cell wall, S. stapfianus initiates alterations in the abundance of transcripts that relate to the response to oxidative stress, response to water deficit, and protein refolding (SI Appendix, Fig. S4).

S. pyramidalis and S. stapfianus transcriptional landscape during desiccation/rehydration. (A and B) Bar plots of the numbers of differentially expressed genes (FDR 0.01) for S. pyramidalis (A) and S. stapfianus (B) from edgeR contrasts of sequential conditions; 2g corresponds to the contrast 2 vs. 3 gH2O g1 dw, 1.5g corresponds to 1.5 vs. 2 gH2O g1 dw, 1g corresponds to 1 vs. 1.5 gH2O g1 dw, and so on. The last S. stapfianus contrast is 24 h after recovery irrigation vs. 3 gH2O g1 dw. The numbers of up- and down-regulated genes are indicated at the top and bottom of each bar, respectively. The skull and bones icon indicates that S. pyramidalis is severely affected when at 1 gH2O g1 dw and enters into senescence. (C and D) Graphs of enriched GO biological process categories in the contrast 2 vs. 3 gH2O g1 dw for S. pyramidalis (C) and S. stapfianus (D). Nodes represent categories and edges represent the parentchild relationships in the ontology. Node identities and positions are identical in both graphs. Color is proportional to the ratio of increased abundance vs. decreased abundance transcripts in the category, with a green color indicating a ratio of more than one (a majority of increased abundance transcripts) and a magenta color indicating a ratio of less than one (a majority of decreased abundance transcripts). Category identifications and names are listed in SI Appendix, Fig. S4.

As dehydration advances from 2 to 1.5 gH2O g1 dw in S. pyramidalis, the functional categories of SDATs remain relatively unchanged from that activated at the initial loss of water, and as it is undergoing senescence during the 1.5 to 1 gH2O g1 dw transition, further acclimation appears unlikely. By contrast, S. stapfianus exhibits an increase to 3,730 SDATs during the 2 to 1.5 gH2O g1 dw transition, but starting at the 1.5 to 1 gH2O g1 dw transition, it initiates a major remodeling of its transcriptome (SI Appendix, Fig. S3), as indicated by a significant increase to 14,557 and 16,047 SDATs during these two transitions in water content, respectively (Fig. 2D). Global transcriptional remodeling continues during the 0.75 to 0.5 gH2O g1 dw transition, albeit at a lower degree, with 8,146 SDATs (Fig. 2D). When desiccated S. stapfianus plants are rehydrated, another strong transcriptome reprogramming, with 27,280 SDATs 12 h after rehydration, is evident and shifts to a transcriptome functional expression profile more similar to that of the fully hydrated control (SI Appendix, Fig. S3). Although S. stapfianus appeared morphologically fully recovered after 24 h of rehydration, the transcriptional profile is not equivalent to that observed in leaves of plants with a water content of 3 gH2O g1 dw (SI Appendix, Fig. S3), with 24,659 SDATs between the two conditions (Fig. 2B). Leaves from plants 24 h after rehydration have up-regulated SDATs classified in ribosome biogenesis GO categories and down-regulated SDATs in photosynthesis categories, as well as remnants of stress-responsive adaptations, including the response to water categories, and altered metabolism, suggested by the presence of glucose 6-phosphate, fructose 1,6-bisphosphate, and several other metabolism-related categories (SI Appendix, Fig. S4B).

To directly compare the transcriptomes for S. stapfianus and S. pyramidalis and identify differentially regulated transcripts that relate to the differences between the two species in the hydrated state prior to dehydration, we created a custom list of syntenic ortholog genes (Materials and Methods). Differential expression was accomplished using a contrast S. stapfianus vs. S. pyramidalis in edgeR (23), and the resultant syntenic ortholog gene lists were probed with GO enrichment as described previously for the intraspecies dehydration transcriptome analyses. The analyses demonstrate that S. stapfianus and S. pyramidalis have very different transcriptional landscapes under hydrated conditions that reflect functionally different priorities for each species. The S. stapfianus transcriptome significantly favors nitrogen, starch, and photosynthetic metabolic processes, whereas the S. pyramidalis transcriptome significantly favors processes involved in growth, primarily the biogenesis of cell wall components (SI Appendix, Fig. S5A). These differences are also reflected at the cellular component and molecular levels (SI Appendix, Fig. S5 B and C), with the majority of cellular functions related to the chloroplast and photosystems in S. stapfianus and the symplast, cytoskeleton, cell wall, and cell wall modification activities in S. pyramidalis.

To further compare the response of S. pyramidalis and S. stapfianus to dehydration, we performed a proteomic analysis using young leaves at 3 and 1.5 gH2O g1 dw and focused on proteins encoded by syntenic genes in a comparison of enriched GO biological process categories of accumulating and decreasing proteins in both water content conditions (SI Appendix, Fig. S6). At 1.5 gH2O g1 dw, S. pyramidalis had increased accumulation of proteins that are almost exclusively involved in stress responses; S. stapfianus had increased accumulation of stress response proteins but also, accumulated proteins involved in the response to misfolded proteins and protein catabolism (SI Appendix, Fig. S6A), and it decreased the abundance of proteins involved in energy production (SI Appendix, Fig. S6B). The protein data demonstrate that, as observed for the transcriptomic profiles, S. pyramidalis and S. stapfianus follow predominantly different approaches of protein accumulation in their response to dehydration.

To explore the evolution of VDT in the Chloridoideae subfamily of grasses, we made use of several high-quality genomes with similar dehydration expression datasets that were available for this group of grasses: the desiccation tolerant (S. stapfianus, O. thomaeum, and E. nindensis) and the DS (E. tef and S. pyramidalis). To facilitate comparisons between species with different ploidy, we clustered genes into syntenic orthologs using MCScan (25) and orthologous groups (orthogroups) using OrthoFinder (26) and compared expression patterns between genes in the same orthogroups. We identified 49,418 orthogroups from OrthoFinder containing 806,075 genes across 23 diverse land plant genomes and focused the subsequent analyses on orthogroups, orthologs, or syntenic gene pairs present in the genome of all chloridoid grasses.

We first surveyed the global expression profiles of the five Chloridoid grasses under well-watered, drought/desiccation, and rehydration conditions using transformed expression data of 19,267 shared syntenic orthologs across all species. We applied a dimensionality reduction on the resulting expression matrix through principal component analysis. The first two principal components collectively explain 62% of the variance and separate the expression datasets by species and stress (Fig. 3). Well-watered RNASeq samples are found in a single tight cluster of all five species, while desiccation and rehydration samples are found in dispersed but distinct clusters. Samples from dehydration and rehydration time courses in the DT species fall into two clusters, with E. nindensis and O. thomaeum samples intertwined in one cluster and S. stapfianus in the second. The dehydration samples from the two DS species (E. tef and S. pyramidalis) clustered together in a third distinct cluster. Samples of E. nindensis and O. thomaeum are separated by relative water content in principal component (PC)1 and by dehydration vs. rehydration in PC2, but interestingly, they are not delineated by species. Together, these results indicate that expression patterns are broadly conserved in leaf samples of all species but that dehydration and rehydration samples are distinct between the three lineages of DT species and their DS relatives.

Dimensional reduction of drought expression profiles across DS and DT Cloridoid grasses. Raw expression values for syntenic orthogroups were transformed by z score prior to principal component analysis. The first two principal components are plotted for the two DS Chloridoid grasses (E. tef and S. pyramidalis) and three tolerant grasses (E. nindensis, O. thomaeum, and S. stapfianus) with comparative expression datasets. Points are colored by species or hydration state as indicated in the key.

The same leaf RNASeq data were analyzed in a pairwise fashion to identify genes with significantly increased transcript abundance under dehydrating conditions in all five species. These SDATs were clustered based on orthogroup using OrthoFinder (as described above) and compared between species. Orthogroups were used in this set of analyses as they contained more genes than the synteny-based analyses, and orthogroups have better resolution of recently duplicated genes. Across the five sequenced chloridoid grasses, the largest number of up-regulated orthogroups under dehydrating conditions was observed between the two Sporobolus species (Fig. 4), as expected since they are sister taxa. The second largest number of up-regulated orthogroups was shared between the two Sporobolus species and O. thomaeum (Fig. 4), which is consistent with their phylogenetic placement within the Chloridoideae. Many other orthogroups are up-regulated similarly in all five species (Fig. 4). The orthogroups uniquely up-regulated in all VDT species are enriched in 214 biological process GO terms (SI Appendix, Fig. S7). Highly enriched GO terms include ultraviolet UV light response, chlorophyll catabolism, reactive oxygen species (ROS) metabolism, seed dormancy maintenance by abscisic acid (ABA), and gene expression in response to heat stress, among others (SI Appendix, Fig. S7A), These GO terms are consistent with well-characterized processes related to DT. Other GO terms with a lower magnitude of enrichment include those related to lipids, osmoprotectant biosynthesis, high light response, energy metabolism, protein degradation, and ABA signaling (SI Appendix, Fig. S7 B and C). Seventy-one biological process GO terms were uniquely up-regulated in only the DS species (SI Appendix, Fig. S8). These included several terms related to salicylic acid as well as ethylene and ABA signaling, arabinose biosynthesis, cell wall biogenesis, and notably, leaf senescence, among others (SI Appendix, Fig. S4). We then asked whether any of the GO terms uniquely up-regulated in DT species would overlap with those uniquely down-regulated in DS species and vice versa (SI Appendix, Table S3). The GO term protein folding was uniquely up-regulated in DT and down-regulated in DS species. Across these five species, most seed-related orthogroups are up-regulated similarly (SI Appendix, Fig. S9). There are no seed orthogroups that are up-regulated in all three DT species without also being up-regulated in one or more DS species.

Venn diagram of up-regulated orthogroups across the five surveyed chloridoid grasses. The number of overlapping orthogroups with up-regulated expression under drought is shown for each comparison.

ELIPs have a conserved role in photoprotection during desiccation, and they have undergone massive tandem gene duplication in all sequenced resurrection plant genomes surveyed to date (14). We observed a similar duplication of ELIPs in the Sporobolus genomes (Fig. 5A). The S. stapfianus genome has 65 ELIPs in three tandem arrays, and the S. pyramidalis genome has 30 ELIPs in two tandem arrays (Fig. 5B). The largest array in S. stapfianus has 49 ELIPs compared with 17 in its corresponding homologous region, suggesting the duplications occurred after the divergence of the two S. stapfianus subgenomes. Both O. thomaeum and S. stapfianus have large tandem arrays of ELIPs, but the duplication events originated from different syntenic orthologs. The total number of ELIPs in S. pyramidalis is higher than some other desiccation-tolerant species, but when gene counts are normalized for ploidy, the ELIPs are within the range of other sensitive grasses.

ELIPs tandem duplication in S. stapfianus and ELIP gene abundance in leaf tissues. (A) Microsynteny of two ELIP tandem arrays is shown in S. stapfianus. ELIPs are shown in red, other genes are shown in gray, and syntenic homeologs between the scaffolds are denoted by gray connections. (B) The number of ELIPs in sequenced Chloridoideae grasses (E. tef, S. stapfianus, S. pyramidalis, E. coracana, O. thomaeum, and Z. mays) is plotted. The two desiccation-tolerant grasses are denoted in red. (C) Log2-transformed gene abundance (TPM) of the 30 ELIPs in S. pyramidalis and 65 ELIPs in S. stapfianus across each replicate of the leaf desiccation time courses.

ELIPs have little to no detectable expression in well-watered tissue, but they are highly induced in desiccating S. stapfianus leaf tissue after they reach 1.0 gH2O g1 dw, and their expression continues 12 and 24 h postrehydration (Fig. 5). ELIPs are also up-regulated under drought in S. pyramidalis, and this occurs quickly in the dehydration process at 2.0 and 1.5 gH2O g1 dw. However, their combined expression is less than S. stapfianus (Fig. 5C), similar to what has been observed in other DS grasses (14).

The genomic resources we developed for the sister species S. stapfianus and S. pyramidalis offer a robust contrast that facilitates a strong comparison between a VDT and a DS grass species. The addition of the genomic resources from other resurrection grasses, O. thomaeum (8) and E. nindensis (10), broadens the comparison further into the Chloridoideae subfamily of grasses. The two genome assemblies revealed the complex mixed ploidy of these two grasses, with S. stapfianus primarily tetraploid and S. pyramidalis primarily hexaploid. The structural complexity of the two genomes likely contributed to the inability to assemble the genomes into chromosome-level contigs or to record sequenced genome sizes equivalent to those determined cytologically. The increase in ploidy between the two species probably occurred immediately after the divergence of the S. pyramidalis clade from the common ancestor of the two species (18). The assemblies did not reveal any genomic structural characteristics, with the exception perhaps of tandem arrays of ELIP genes (14), that could be attributed to the difference in VDT between the two species, which is consistent with the general observation that there is not a genomic blueprint for VDT in resurrection species (4). However, the assemblies did allow for a thorough comparative analysis, both structural and functional, of the gene space for each genome, and coupled with the in-depth transcriptome data, we were able to explore a detailed genomic assessment of the dehydration/desiccation responses within the Sporobolus sister species contrast.

The generation of transcriptomic and proteomic data for dehydrating young leaf tissue at specific water contents during a dry-down experiment such that the dehydration levels are survivable for both grasses provides a broad assessment of the stress response for each species. DS S. pyramidalis mounted a messenger RNA (mRNA)-level response to an initial drop in hydration as has been observed for the majority of dehydration-sensitive plants (27, 28). However, as dehydration to 1.5 gH2O g1 dw was reached, the transcript abundance response declined dramatically (Fig. 2A), perhaps as the leaf water content reached a critical level for S. pyramidalis. The leaves of S. pyramidalis are wilted at 1.5 gH2O g1 dw (21) but otherwise, appear undamaged, so it is tempting to speculate that the decline in the transcript abundance response may be related to wilting and perhaps, loss of turgor during wilting in S. pyramidalis. Although S. pyramidalis responds quickly to a loss of water, the early increased transcript abundance response appears to be focused on protein translational processes and transcripts common to heat and cold stress (SI Appendix, Fig. S4), and only later, as dehydration deepens, do transcripts associated with proline metabolism (osmoregulation) and redox proteins, common to water-deficit responses (27), accumulate. The early decline in transcripts involved in photosynthesis and cell wall homeostasis is also common to the dehydration response in most angiosperms (4). The later decline in transcripts that are associated with general biosynthetic processes is consistent with the general lack of a metabolic response to dehydration seen in metabolite profiling studies of S. pyramidalis at similar levels of water loss (21). Desiccation-tolerant S. stapfianus, in contrast, exhibited a significantly different qualitative transcriptional response to dehydration with a low-magnitude response in the early phase of dehydration. With the comparatively muted response and although there are some common transcript abundance responses between the two species, S. stapfianus clearly targets remodeling a completely different functional aspect of the transcriptome than does S. pyramidalis at similar water contents. Indeed, it appears that S. stapfianus targets the accumulation of transcripts that function more in stress-related activities unlike S. pyramidalis, which does not. The differences between the two transcriptional responses for the two species were unexpected as other studies have indicated that there was extensive overlap in functionality of the transcriptomes of both sensitive and tolerant grasses exposed to dehydration (10). Although there are a few common transcript abundance functional categories in the early response to dehydration in both species, it is clear that the overall transcriptome remodeling during dehydration is very different between them, as exemplified by the different dehydration thresholds for the accumulation ELIP transcripts.

For S. stapfianus, the primary remodeling of the transcriptome during dehydration appears to occur as the plants reach the 1.0- to 0.75-gH2O g1 dw part of the drying curve, which appears to be a critical period in the desiccation response of all resurrection angiosperms studied so far (29) and concurs with early microarray data (30). In S. stapfianus, the transition from 1.0 to 0.75 gH2O g1 dw occurs during leaf curling (19) and is likely at water contents just prior to and during a change in membrane fluidity that occurs as leaf water potentials approach 12 MPa (4). The functional aspects of the transcriptome remodeling during desiccation of S. stapfianus leaves have been documented previously and are in accord with the observation that transcript abundance is concordant with changes in metabolism associated with cellular protection aspects of DT (30). There was a dramatic alteration of the transcriptome upon rehydration of S. stapfianus leaves, which likely reflects the complex nature of the dehydration event. The magnitude of the change in the transcriptome, reflecting a change in abundance of at least half of the known transcripts, and the functional processes they represent indicate not only the stress incurred from the inrush of water and mechanical aspects of cellular expansion but also, the need to repair damage (from both desiccation and rehydration), reactivate energy metabolism, and reinstate the physiological integrity of the cells and tissues (4). The observation that transcripts encoding proteins involved in ribosome biogenesis are accumulated and those encoding proteins involved in photosynthesis have not recovered control levels at 24 h following rehydration highlights the extent of the impact that desiccation and rehydration have on plant cells and tissues even in DT plants. S. stapfianus requires between 48 and 72 h to regain the structural and physiological integrity seen in well-watered plants (19, 31).

The remodeling of the transcriptome in response to dehydration starts from two very different resting-state (fully hydrated) transcriptomes. Our functional analysis of the gene-level expression of the syntenic orthologs of the sister grasses, although somewhat confounded by the structural complexity of the two genomes, revealed that for S. stapfianus, the biosynthesis of starch and nitrogen compounds was perhaps a priority for young leaves under normal conditions, while for young leaves of S. pyramidalis, the priority appeared more focused on the construction of cell walls. Although somewhat speculative, the increase in nitrogen compounds, primarily amino acids from a combination of new synthesis and redistribution, was the focus of a recent study that demonstrated that these compounds are apparently used to fuel central metabolism or for other metabolic adjustments related to the acquisition of DT, such as osmoregulation (32). The differences in priorities are consistent with the changes in protein abundance from 3 to 1.5 gH2O g1 dw. Although S. pyramidalis protein abundance changes did not reflect cell wall processes, perhaps due to the difficulty in extracting the majority of wall-related proteins (33), they show that S. pyramidalis was almost exclusively focused on the accumulation of stress response proteins. At the same desiccation stage, S. stapfianus had similarly accumulated stress response proteins but also, proteins involved in protein catabolism, and it had down-accumulated energy-related proteins, suggesting a scaling down, at the protein level, of the energy metabolism transcriptomic activity of the hydrated state and the continuation of N metabolism prioritization through protein salvage, possibly from misfolded proteins. Syntenic orthologs transcriptomic data are also consistent with information from the metabolomes of young leaves of these two grasses in that fully hydrated leaves of S. stapfianus were focused on the accumulation of a variety of amino acids and photosynthate derivatives, while for S. pyramidalis, the metabolome was focused on energy metabolism and growth (21). The conclusion from the metabolomics analyses was that leaves of S. stapfianus were prepared (primed) for a dehydration/desiccation event by accumulating osmolytes in times of water abundance and that S. pyramidalis needed to generate energy and components to support a faster growth rate, perhaps to deal with competition in its more mesic habitats. The hydrated transcriptome functional analysis fully supports this conclusion, and our transcriptomic and proteomic data, although somewhat speculative in nature, extend the hypothesis to include a focus on the maintenance of chloroplast function in S. stapfianus in the priming mechanism and cell wall biogenesis in S. pyramidalis as a target for the focus on energy metabolism and growth.

Although transcriptomic analyses were useful in comparing the functional aspects of the response to dehydration of the contrasting sister Sporobolus species and the desiccation and rehydration response of S. stapfianus, the availability of a high-quality genome for each of these two species allowed for a direct comparison of the genetic components (and their functions) of the response and allowed us to extend the comparison with other desiccation-tolerant and DS grass species. The broad comparison of the expression patterns of orthogroups and syntenic gene sets common in all five of the chloridoid grasses included in the analysis confirmed the disparate nature of the dehydration response between S. stapfianus and S. pyramidalis. It also revealed that the overall dehydration expression pattern for S. stapfianus was distinctly different from those observed for the other two desiccation-tolerant grasses, E. nindensis and O. thomaeum. The most recent phylogenetic analyses of the Chloridoideae indicate that the common ancestor of the Eragrostideae, which contains E. nindensis and E. tef, gave rise to the Zoysieae and the Cynodonteae, within which O. thomaeum resides; the Zoysieae then diversified into the Zoysiinae and the Sporobolinae, within which the Sporobolus clade containing both S. stapfianus and S. pyramidalis is located (18, 34). The phylogeny indicates that O. thomaeum and S. stapfianus are closer to one another than either are to E. nindensis, which is consistent with results of our analysis of orthogroups representing SDATs that increase in abundance. However, the overall expression response to dehydration for O. thomaeum appears to be more similar to the distantly (ancestrally) evolved response of E. nindensis. This might also explain why there is less overlap between the dehydration transcriptome of S. stapfianus and the transcriptomes of both sensitive and tolerant grasses exposed to dehydration (10). Thus, although we have used only a three-way comparison, it does allow for the hypothesis that the desiccation response of S. stapfianus represents a more recent evolution of a mechanism for VDT within the Chloridoideae.

The orthogroup analysis of the SDATs that increase in abundance in all of the VDT species underscored the importance of most of the well-characterized processes that deliver cellular DT (4). The orthogroup analysis of the SDATs that increase in abundance in all of the DS species also reconfirmed what we understand of the response of most plants to a water deficit stress and highlighted the induction of senescence, which is thought to be blocked in resurrection angiosperms during desiccation (reviewed in ref. 4). However, the observation that transcripts classified as involved in protein folding accumulate in the VDT species and decline in abundance in the DS species indicates not only that maintaining protein structure is important in VDT, as has been well documented, but that the lack of the necessary components to do so might be critical in rendering a plant DS. The observation that all seed-related orthogroups are up-regulated in all VDT species and in one or more of the DS species reinforces the hypothesis that VDT likely evolved from a reprogramming of DT mechanisms that evolved in seeds (10).

S. stapfianus Gandoger (original provenance: Verena, Transvaal, South Africa) and S. pyramidalis Beauv. (also known as Sporobolus indicus var. pyramidalis) were grown and maintained as described in ref. 21. For genome sequencing, a single, healthy 3-mo-old fully hydrated plant from each species was selected, and young leaf tissue was collected, flash frozen in liquid N2, and stored at 80C. For RNASeq experiments, seeds were collected from selfed clonal plants derived from the individuals used for the genome sequencing and germinated and plants grown to the 3-mo-old stage under greenhouse conditions (16-h light and day/night temperatures of 28C/19C).

Plants were grown and maintained and seed stocks were increased (as described in ref. 35) in 1-gallon pots under greenhouse conditions. Three-month-old plants were subjected to a drying event by withholding water. S. stapfianus plants were dried until desiccated (after 3 wk), whereas S. pyramidalis plants were dried to a water content of 1.5 gH2O g1 dw before rewatering. Drying rates were as described by Oliver etal. (21) to simulate field drying rates that occur over a period of 7 d to reach the 1.5-gH2O g1 dw stage for both grasses and 14 d for full desiccation of S. stapfianus (plants were left dry for a further 7 d). Young leaf tissue was collected at daily intervals, between 9 and 10 AM, from individual plants, flash frozen in liquid N2, and stored at 80C. Dried plants were maintained dry for a week before rehydration. Duplicate samples were harvested for water content measurements at the time of sampling. The water content was calculated as fresh weight minus the dry weight (dried to equilibrium at 70C for 4 h). Triplicate samples were chosen for RNA extraction. Rehydration was achieved by placing the desiccated S. stapfianus plants under a continuous misting system in the greenhouse, and young leaves were sampled in triplicate at 12 and 24 h following the addition of water.

The genome size was estimated using the one-step flow cytometry procedure described in ref. 36. Approximately 1 cm2 of leaf material from the Sporobolus species and leaf material of the calibration standard Petroselinum crispum (Mill.) Fuss (37) (haploid genome [1C] value = 2,201 Mbp) were diced in 1 mL of general purpose buffer (GPB) (38) supplemented with 3% polyvinylpyrrolidone of average molecular weight of 40,000. A further 1 mL of GPB was added, and the homogenate was filtered through a 30-m nylon mesh (Celltrics 30-M mesh; Sysmex); 100 L propidium iodide (1 mg/mL) was added and incubated on ice for 10 min. The relative fluorescence of 5,000 particles was recorded using a Partec Cyflow SL3 flow cytometer (Partec GmbH) fitted with a 100-mW green solid-state laser (532 nm; Cobolt Samba). Three replicates of species were processed, and output histograms were analyzed using FlowMax software v.2.4 (Partec GmbH).

Highmolecular weight DNA was isolated from 5 g of flash-frozen young leaf tissue using the PacBio SampleNetShared Protocol (https://www.pacb.com/support/documentation/) as described. Random shotgun genomic libraries with various insert sizes, both paired end and mated pair libraries, were constructed for the Illumina HiSeq 2000 sequencing system (Illumina) according to the manufacturers protocols. Sequencing of was conducted using an Illumina HiSeq 2500 ultrahigh-throughput DNA sequencing platform (Illumina) at the DNACore facility at the University of Missouri, Columbia, MO (https://dnacore.missouri.edu/ngs.html).

For Chicago sequencing, genomic DNA isolation, library preparation, sequencing, and assembly were conducted by Dovetail Genomics and are detailed in SI Appendix, Methods. Chicago genomic DNA libraries were prepared as described in ref. 39. Dovetail Hi-C libraries were prepared as described in ref. 40 after fixation of chromatin in place in the nucleus by incubation of leaf tissue for each species in 1% formaldehyde for 15 min under vacuum.

A de novo assembly was constructed using a combination of paired end (mean insert size 350 bp) libraries and mated pair libraries with inserts ranging from 7 to 12 kbp. De novo assembly was performed using Meraculous v2.2.2.5 (diploid mode 1) (41) with a k-mer size of 109. Reads were trimmed for quality, sequencing adapters, and mate pair adapters using Trimmomatic (42). The de novo assembly, shotgun reads, Chicago library reads, and Dovetail Hi-C library reads were used as input data for HiRise, a software pipeline designed specifically for using proximity ligation data to scaffold genome assemblies (39) and detailed in SI Appendix, Methods.

RNA was extracted from young leaf samples using the RNeasy (Qiagen) kit with RLC buffer following the manufacturers protocol. The RNA isolates were treated with deoxyribonuclease 1and cleaned using the DNA-free RNA Kit (Zymo Technologies). RNA quality was assessed by use of a fragment analyzer (Advanced Analytical Technologies), and concentration was determined with a Nanodrop Spectrophotometer (ThermoFisher). RNA libraries were individually bar-coded from 2.7 g of template total RNA utilizing the TruSeq RNA Sample Prep Kit (Illumina) as described in the manufacturers protocol. Libraries were pooled in groups of 12 and sequenced (12 samples per lane) on an Illumina HiSeq 2500 ultrahigh-throughput DNA sequencing platform (Illumina) at the DNACore facility at the University of Missouri.

High-quality RNA was extracted from whole-root tissues obtained from seedlings at the four-leaf stage when the first pair of leaves had matured, whole seedlings at the two-leaf stage, mature leaves, young leaves, floral inflorescences, and tissue samples identical to those used for the dehydration/desiccation/rehydration transcriptomes. The RNAs were pooled for each individual species for subsequent amplification. Bar-coded SMRT libraries were prepared and sequenced on the PacBio platform with X SMRT cells by Novogene Corporation Inc. Sequence reads were processed using Iso-Seq3 (https://github.com/PacificBiosciences/IsoSeq).

Genome assemblies were annotated using three rounds of MAKER-P. Briefly, round 1 used full-length nonchimeric sequences from PacBio transcriptome sequencing as EST evidence; a collection of Arabidopsis thaliana [Araport11 (43)], Zea mays [downloaded from Gramenes ftp server at https://www.gramene.org/ftp-download; AGPv4 release 59 (44, 45)], Sorghum bicolor [downloaded from Phytozome; https://phytozome-next.jgi.doe.gov/pz/portal.html, version 3.1.1 (46)], and O. thomaeum [downloaded from Phytozome, version 1.0 (7)] sequences as protein evidence; and a de novo repeats library obtained using LTR_Finder (47), LTRharvest (48), LTR retriever (49), and RepeatModeler (50) as inputs. Round 2 used the round 1 maker gff file and an SNAP (http://korflab.ucdavis.edu/software.html) hmm file obtained from the round 1 gff3 file. Round 3 used the round 2 maker gff3 file, the GeneMark-ES (51) HMM output file from a BRAKER (52) run from hisat (53) aligned RNASeq reads, and the corresponding Augustus (54) gene prediction models.

As a further filter, we decided to only keep genes that had expression evidence in our RNASeq Illumina or Pacbio data and/or whose corresponding protein is homologous to a known plant protein. Evidence of expression was at least one of the following two criteria: 1) an expression value of at least one transcripts per million (TPM) in all replicates of at least one sample in the RNASeq data after bowtie2 (55) alignment and Salmon (56) quantification or 2) at least one TPM in the gtf file obtained after a minimap2 (57) alignment and StringTie (58) quantification of IsoSeq3 polished long reads. Sporobolus proteins were considered as homologous if they satisfied at least one of three criteria: 1) a blastp match with an e value of 1e-6 or lower vs. either Arabidopsis proteins [Araport11 annotation (43)]; 2) vs. a collection of Glycine max, Oryza sativa subsp. japonica, Populus trichocarpa, Solanum lycopersicum, S. bicolor, Vitis vinifera, Brachypodium distachyon, Physcomitrella patens subsp. patens, and Chlamydomonas reinhardtii UniProt Trembl proteins; or 3) proteins with a domain identified by InterProScan (59) with an e value of 1e-10 or lower.

Final gene identifiers are in the format Sp2s00000_00000 for S. pyramidalis and Ss2s00000_00000 for S. stapfianus. Sp stands for S. pyramidalis, Ss stands for S. stapfianus, 2 indicates the genome version, s00000 indicates the scaffold number, and the last five digits are an arbitrary gene number.

GO annotation was done using a simplified version of the maizeGAMER pipeline (60). Transcript sequences were analyzed using BLAST vs. Arabidopsis Araport11 proteins and a collection of UniProt (61) TREMBL proteins from nine plant species (G. max, O. sativa subsp. japonica, P. trichocarpa, S. lycopersicum, S. bicolor, V. vinifera, B. distachyon, P. patens subsp. patens, C. reinhardtii), InterProScan with the -goterms option, and Pannzer2 (62). GO annotations of BLAST reciprocal best hits were retrieved from either the A. thaliana gaf file available at http://geneontology.org or the GOA file available at European Bioinformatics Institute. GO annotations from Blast, InterProScan, and Pannzer2 analyses were collated into a nonredundant gaf file and used for GO enrichment analyses.

Comparative genomics analyses were completed using MCScan (25). The O. thomaeum genome was used as a common anchor as it is diploid and has a chromosome scale assembly. A minimum cutoff of five genes was used to identify syntenic gene blocks. A set of syntenic orthogroups was created containing genes present in all grass species analyzed.

We clustered proteins from 23 species into orthogroups using OrthoFinder (v2.3.8) (26). OrthoFinder using default parameters and the reciprocal DIAMOND search was used to identify similar proteins, which were clustered using the Markov Cluster Algorithm. The following species were included in OrthoFinder: Ananas comosus, A. thaliana, B. distachyon, E. nindensis, E. tef, L. brevidens, L. subracemosa, Marchantia polymorpha, Medicago truncatula, O. sativa, O. thomaeum, P. patens, S. bicolor, Setaria italica, Selginella. lepidophylla, Selaginella. moellendorffii, S. lycopersicum, S. pyramidalis, S. stapfianus, V. vinifera, Xerophyta viscosa, Zostera marina, and Z. mays.

A set of orthogroups containing seed-related genes was previously identified based on seed and leaf expression datasets from Z. mays, S. bicolor, O. sativa, and E. tef (22). Syntenic orthologs of these seed-related genes were then identified in O. thomaeum, and these syntenic orthologs were used with OrthoFinder output to identify seed-related orthogroups.

Differential expression (DE) analyses were conducted using DESeq2 (63) (E. nindensis, E. tef, and O. thomaeum) or edgeR (23) (S. stapfianus and S. pyramidalis), and resulting outputs were processed using Pandas 0.25.0 in Python 3.6.8. Up-regulated and down-regulated genes were extracted for each species (SI Appendix, Table S2). OrthoFinder output was used to identify the orthogroup corresponding to each gene in the differential expression output. For seed orthogroups, the previously generated lists of seed-related orthogroups were used to extract differentially expressed seed orthogroups. The intersections and differences among the resulting sets of orthogroups were then extracted, and Venn diagrams were constructed using matplotlib_venn (version 3.1.1) (64) or Python package venn. Enrichment of GO terms was conducted using topGO (65) 2.38.1 in R 3.6.0 for various intersections and differences of DE orthogroups (SI Appendix, Table S3). Differentially expressed genes in these orthogroups were extracted, and GO enrichment was conducted using Fishers exact test via the weight01 algorithm. Following enrichment, unique biological process GO terms were extracted using the Python library Pandas. Unique GO terms for DS as compared with DT were also extracted for further study.

A comparison of gene expression of S. stapfianus vs. S. pyramidalis leaves at 3 gH2O g1 dw was achieved using tximport (66) and edgeR (23). We created a custom syntenic orthologs tx2gene file (https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html). GO annotation files for both species were merged, replacing each gene identifier with the custom gene identifier from our tx2gene file. In this way, each gene inherits the GO annotation of all its corresponding S. stapfianus and S. pyramidalis genes (SI Appendix, Methods). GO categories enrichment analysis was carried out for the list of up-regulated both_n genes and the list of down-regulated both_n genes using Bingo (24) in Cytoscape (67), with a false discovery rate (FDR)-adjusted P value cutoff of 0.05 and the list of genes in our tx2gene file as the universe.

Proteins were extracted from triplicate samples of 1 g of frozen leaf tissue, separated on 16-cm sodium dodecyl sulfate polyacrylamide gel electrophoresisgels, and cut into 10 equal slices; each slice was digested with trypsin, and liquid chromatograph mass spectrometer (LCMS) data were acquired on the LTQ Orbitrap at the Charles W. Gehrke Proteomics Center, University of Missouri using standard protocols (http://proteomics.missouri.edu/protocols.php). Raw data were analyzed with MaxQuant software v. 2.0.1.0 (68). Tandem mass spectrometer spectra were searched against the S. pyramidalis and S. stapfianus proteins, and potential contaminants by the built-in Andromeda search engine (69). Label-free quantification (LFQ) of the identified proteins was performed using normalized LFQ (LFQ intensity) using the MaxLFQ algorithms (70). The resulting identified proteins were filtered, keeping only proteins with an LFQ intensity greater than zero in all biological replicates or absent in all biological replicates. Proteins with significant Students t test (two tailed; P < 0.05) results were considered up accumulated (log2 fold change > 0.5) or down accumulated (log2 fold change < 0.5). The lists of up-and down-accumulated protein identifiers were translated to their corresponding syntenic ortholog identifiers, and GO biological process categories enrichment was done using Bingo previously.

We acknowledge the expert technical assistance of Jim Elder in the preparation and growth of the plant material. We also thank Dr. Brian Mooney and the Charles W Gehrke Proteomics Center for their expertise in the proteomics analysis. This work was partially supported by Governor University Research Initiative Program of the State of Texas Grant 05-2018 (to L.R.H.E.), NSF Grant MCB1817347 (to R.V.), and Agricultural Research Services Project 5070-21000-038-00D (to M.J.O.).

Author contributions: E.L., L.R.H.E., R.V., and M.J.O. designed research; J.P., R.F.P., T.H.-H., H.T., and M.J.O. performed research; R.A.C.M., A.H., J.P., R.F.P., U.K.D., A.T.S., T.H.-H., V.S., H.T., E.L., L.R.H.E., R.V., and M.J.O. analyzed data; and R.A.C.M., A.H., L.R.H.E., R.V., and M.J.O. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2118886119/-/DCSupplemental.

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A comparative genomics examination of desiccation tolerance and sensitivity in two sister grass species - pnas.org

A new wide-ranging report Genomics Beyond Health published today by the Government Office for Science investigates how genomics could play a part in our lives in the future, from sport to education and tackling crime.

Until now genomics has mostly been used within healthcare and medical research where it can help provide more precise diagnosis, target better treatments, and help predict the risks of developing certain disease. The UKs use of genomics in healthcare is world-leading and viral genomics has been critical for monitoring COVID-19 and detecting emerging variants.

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Sir Patrick Vallance, Government Chief Scientific Adviser, said:

We are still in the infancy of understanding the complexity of genomic data but this is changing very rapidly. Now is the time to consider what might be possible, and what actions government and the public could take to ensure the widespread application of genomics can occur in a way that protects and benefits us all. This report looks at the current landscape of genomics, investigates how the science is developing, and looks at what is possible now, what might be possible in the future.

George Freeman, Minister for Science, Research and Innovation, said:

Since we launched the UK Genomics Healthcare program in 2011, the UK has grown into a global powerhouse in genomic healthcare, from diagnostics to drugs and vaccines. But this is just the start of the genomic revolution. As this timely report shows, our growing understanding of the genetic code of life opens up exciting new opportunities from drought and disease resistant crops to harnessing cells or factories, and new net zero biofuels and marine agriculture. To unlock these opportunities, we need to lead in both the science and the ethics and reputation for consumer confidence and public support.

Professor Ewan Birney, EMBL Deputy Director General and Director of EMBLs European Bioinformatics Institute (EMBL-EBI) said:

Genomics has the potential to transform the world we live in, and help us tackle some of the greatest challenges facing our species and planet. This report is a timely reminder that policy makers and the public need the right information at the right time, to understand and exploit the insights these new technologies provide.

While some of the potential uses of genomics may not be realised in the short or even medium-term, people are already exploring new ways to use genomic information today. To keep pace with the science, policy will need to consider areas such as data inequality, privacy and regulation.

Thirty subject and policy experts in science and technology across academia and government have contributed to this report. To request interviews or comment from contributors please contact goscomms@go-science.gov.uk.

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Genomics Beyond Healthcare: future uses and considerations of genomic science - GOV.UK

The world class Single-Cell Genome Sequencing Market document encompasses a thorough study of current situation of the global market along with several market dynamics. To formulate this report, detailed market analysis has been performed with the inputs from industry experts. Depending on clients demand, huge amount of business, product and market related information has been brought together via this report that eventually helps businesses create better strategies. All of these features are strictly applied while building Single-Cell Genome Sequencing Market research report for a client. It gives explanation about various definitions and segmentation or classifications of the industry, applications of the industry and value chain structure.

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Major market manufacturers enlisted in this report are:

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TheSingle-Cell Genome Sequencing Market is segmented on the basis of product, wound type and end user. The growth amongst these segments will help you analyze meager growth segments in the industries, and provide the users with valuable market overview and market insights to help them in making strategic decisions for identification of core market applications.

The market report is segmented into the application by the following categories:

Global Single-Cell Genome Sequencing Market, By Type (Instruments and Reagents), Technology (NGS, PCR, Q-PCR, Microarray and MDA), Workflow (Single Cell Isolation, Sample Preparation and Genomic Analysis), Disease Area (Cancer, Immunology, Prenatal Diagnosis, Neurobiology, Microbiology and Others), Application (Circulating Cells, Cell Differentiation, Genomic Variation, Subpopulation Characterization and Others), End User (Academic and Research Laboratories, Biotechnology and Biopharmaceutical Companies, Clinics and Others), Country (U.S., Canada, Mexico, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia- Pacific, Brazil, Argentina, Rest of South America, South Africa, Saudi Arabia, UAE, Egypt, Israel, Rest of Middle East and Africa) Industry Trends and Forecast to 2029

Highlights Major Key Factors in Single-Cell Genome Sequencing Market Report:

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Competitive Rivalry:

The Plasma Treatment System research report includes an analysis of the competitive landscape present in the Single-Cell Genome Sequencing Market. It includes an assessment of the existing and upcoming trends that players can invest in. Furthermore, it also includes an evaluation of the financial outlooks of the players and explains the nature of the competition.

Key questions answered in the report include:

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Single-Cell Genome Sequencing Market 2022 Comprehensive Analysis, Business Growing Strategies, Industry Segmentation and Forecast 2029 The Oxford...

By Wyss Institute for Biologically Inspired Engineering at HarvardJanuary 24, 2022

A collaborative research team at Harvards Wyss Institute and the ETH Zurich in Switzerland has identified genomic safe harbors (GSHs) in the tumultuous sea of human genome sequence to land therapeutic genes in. As part of their validation, they inserted a fluorescent GFP reporter gene into candidate GSHs and followed its expression over time. The GSHs could enable safer and longer-lasting expression of genes in future gene and cellular therapies. This illustration won the team the cover of the Cell Reports Methods issue the study is published in. Credit: Erik Aznauryan

Researchers at Harvards Wyss Institute, Harvard Medical School, and the ETH Zurich predict and validate genomic safe harbors for therapeutic genes, enabling safer, more efficient, and predictable gene and cell therapies.

Many future gene and cell therapies to treat diseases like cancer, rare genetic and other conditions could be enhanced in their efficacy, persistence, and predictability by so-called genomic safe harbors (GSHs). These are landing sites in the human genome able to safely accommodate new therapeutic genes without causing other, unintended changes in a cells genome that could pose a risk to patients.

However, finding GSHs with potential for clinical translation has been as difficult as finding a lunar landing site for a spacecraft which has to be in smooth and approachable territory, not too steep and surrounded by large hills or cliffs, provide good visibility, and enable a safe return. A GSH, similarly, needs to be accessible by genome editing technologies, free of physical obstacles like genes and other functional sequences, and allow high, stable, and safe expression of a landed therapeutic gene.

Thus far, only few candidate GSHs have been explored and they all come with certain caveats. Either they are located in genomic regions that are relatively dense with genes, which means that one or several of them could be compromised in their function by a therapeutic gene inserted in their vicinity, or they contain genes with roles in cancer development that could be inadvertently activated. In addition, candidate GSHs have not been analyzed for the presence of regulatory elements that, although not being genes themselves, can regulate the expression of genes from afar, nor whether inserted genes change global gene expression patterns in cells across the entire genome.

Now, a collaboration of researchers at Harvards Wyss Institute for Biologically Inspired Engineering, Harvard Medical School (HMS), and the ETH Zurich in Switzerland, has developed a computational approach to identify GSH sites with significantly higher potential for the safe insertion of therapeutic genes and their durable expression across many cell types. For two out of 2,000 predicted GSH sites, the team provided an in-depth validation with adoptive T cell therapies and in vivo gene therapies for skin diseases in mind. By engineering the identified GSH sites to carry a reporter gene in T cells, and a therapeutic gene in skin cells, respectively, they demonstrated safe and long-lasting expression of the newly introduced genes. The study is published in Cell Reports Methods.

While GSHs could be utilized as universal landing platforms for gene targeting, and thus expedite the clinical development of gene and cell therapies, so far no site of the human genome has been fully validated and all of them are only acceptable for research applications, said Wyss Core Faculty member George Church, Ph.D., a senior author on the study. This makes the collaborative approach that we took toward highly-validated GSHs an important step forward. Together with more effective targeted gene integration tools that we develop in the lab, these GSHs could empower a variety of future clinical translation efforts. Church is a leader of the Wyss Institutes Synthetic Biology Platform, and also the Robert Winthrop Professor of Genetics at HMS and Professor of Health Sciences and Technology at Harvard University and the Massachusetts Institute of Technology (MIT).

The researchers first set up a computational pipeline that allowed them to predict regions in the genome with potential for use as GSHs by harnessing the wealth of available sequencing data from human cell lines and tissues. In this step-by-step whole-genome scan we computationally excluded regions encoding proteins, including proteins that have been involved in the formation of tumors, and regions encoding certain types of RNAs with functions in gene expression and other cellular processes. We also eliminated regions that contain so-called enhancer elements, which activate the expression of genes, often from afar, and regions that comprise the centers and ends of chromosomes to avoid mistakes in the replication and segregation of chromosomes during cell division, said first-author Erik Aznauryan, Ph.D. This left us with around 2,000 candidate loci all to be further investigated for clinical and biotechnological purposes.

Aznauryan started the project as a graduate student with other members of Sai Reddys lab at ETH Zurichs Department of Biosystems Science and Engineering before he visited the Church lab as part of his graduate work, where he teamed up with Wyss Technology Development Fellow Denitsa Milanova, Ph.D. He since has joined Churchs group as a Postdoctoral Fellow. Reddy, senior and lead author of the collaborative study, is an Associate Professor of Systems and Synthetic Immunology at ETH Zurich and focuses on developing new methods in systems and synthetic biology to engineer immune cells for diverse research and clinical applications.

Out of the 2,000 identified GSH sites, the team randomly selected five and investigated them in common human cell lines by inserting reporter genes into each of them using a rapid and efficient CRISPR-Cas9-based genome editing strategy. Two of the GSH sites allowed particularly high expression of the inserted reporter gene in fact, significantly higher than expression levels achieved by the team with the same reporter gene engineered into two earlier-generation GSHs. Importantly, the reporter genes harbored by the two GSH sites did not upregulate any cancer-related genes, said Aznauryan. This also can become possible because regions in the genome distant from one another in the linear DNA sequence of chromosomes, but near in the three-dimensional genome, in which different regions of folded chromosomes touch each other, can become jointly affected when an additional gene is inserted.

To evaluate the two most compelling GSH sites in human cell types with interest for cell and gene therapies, the team investigated them in immune T cells and skin cells, respectively. T cells are used in a number of adoptive cell therapies for the treatment of cancer and autoimmune diseases that could be safer if the receptor-encoding gene was stably inserted into a GSH. Also, skin diseases caused by harmful mutations in genes controlling the function of cells in different skin layers could potentially be cured by insertion and long-term expression of a healthy copy of the mutated gene into a GSH of dividing skin cells that replenish those layers.

We introduced a fluorescent reporter gene into two new GSHs in primary human T cells obtained from blood, and a fully functional LAMB3 gene, an extracellular protein in the skin, into the same GSHs in primary human dermal fibroblasts, and observed long-lasting activity, said Milanova. While these GSHs are uniquely positioned to improve on levels and persistence of gene expression in parent and daughter cells for therapeutics, I am particularly excited about emerging gain-of-function cellular enhancements that could augment the normal function of cells and organs. The safety aspect is then of paramount importance. With an entrepreneurial team at the Wyss, Milanova is developing a platform for genetic rejuvenation and enhancements with a focus on skin rejuvenation.

An extensive sequencing analysis that we undertook in GSH-engineered primary human T cells clearly demonstrated that the insertion has minimal potential for causing tumor-promoting effects, which always is a main concern when genetically modifying cells for therapeutic use, said Reddy. The identification of multiple GSH sites, as we have done here, also supports the potential to build more advanced cellular therapies that use multiple transgenes to program sophisticated cellular responses, this is especially relevant in T cell engineering for cancer immunotherapy.

This collaborative interdisciplinary effort demonstrates the power of integrating computational approaches with genome engineering while maintaining a focus on clinical translation. The identification of GSHs in the human genome will greatly augment future developmental therapeutics efforts focused on the engineering of more effective and safer gene and cellular therapies, said Wyss Founding Director Donald Ingber, M.D., Ph.D., who is also the Judah Folkman Professor of Vascular Biology at HMS and Boston Childrens Hospital, and Professor of Bioengineering at the Harvard John A. Paulson School of Engineering and Applied Sciences.

Reference: Discovery and validation of human genomic safe harbor sites for gene and cell therapies by Erik Aznauryan, Alexander Yermanos, Elvira Kinzina, Anna Devaux, Edo Kapetanovic, Denitsa Milanova, George M. Church and Sai T.Reddy, 14 January 2022, Cell Reports Methods.DOI: 10.1016/j.crmeth.2021.100154

Additional authors on the study are Alexander Yermanos, Ph.D, and Edo Kapetanovic, members of Reddys group; Anna Devaux at the University of Basel, Switzerland; and, Elvira Kinzina at the McGovern Institute for Brain Research at MIT. The study was supported by ETH Research Grants, the Helmut Horten Stiftung and Aging and Longevity-Related Research Fund at HMS, as well as a Genome Engineer Innovation Grant 2019 from Synthego to Aznauryan.

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Landing Therapeutic Genes Safely in the Human Genome Improving Gene and Cell Therapies - SciTechDaily

The Earth Biogenome Project, a global consortium that aims to sequence the genomes of all complex life on Earth (some 1.8 million described species) in 10 years, is ramping up.

The projects origins, aims, and progress are detailed in two multi-authored papers published this week. Once complete, it will forever change the way biological research is done.

Specifically, researchers will no longer be limited to a few model species and will be able to mine the DNA sequence database of any organism that shows interesting characteristics. This new information will help us understand how complex life evolved, how it functions, and how biodiversity can be protected.

The project was first proposed in 2016, and I was privileged to speak at its launch in London in 2018. It is currently in the process of moving from its startup phase to full-scale production.

The aim of phase one is to sequence one genome from every taxonomic family on Earth, some 9,400 of them. By the end of 2022, one-third of these species should be done. Phase two will see the sequencing of a representative from all 180,000 genera, and phase three will mark the completion of all the species.

The grand aim of the Earth Biogenome Project is to sequence the genomes of all 1.8 million described species of complex life on Earth. This includes all plants, animals, fungi, and single-celled organisms with true nuclei (that is, all eukaryotes).

While model organisms like mice, rock cress, fruit flies, and nematodes have been tremendously important in our understanding of gene functions, its a huge advantage to be able to study other species that may work a bit differently.

Many important biological principles came from studying obscure organisms. For instance, genes were famously discovered by Gregor Mendel in peas, and the rules that govern them were discovered in red bread mold.

DNA was discovered first in salmon sperm, and our knowledge of some systems that keep it secure came from research on tardigrades. Chromosomes were first seen in mealworms and sex chromosomes in a beetle (sex chromosome action and evolution has also been explored in fish and platypus). And telomeres, which cap the ends of chromosomes, were discovered in pond scum.

Comparing closely and distantly related species provides tremendous power to discover what genes do and how they are regulated. For instance, in another PNAS paper, coincidentally also published this week, my University of Canberra colleagues and I discovered Australian dragon lizards regulate sex by the chromosome neighborhood of a sex gene, rather than the DNA sequence itself.

Scientists also use species comparisons to trace genes and regulatory systems back to their evolutionary origins, which can reveal astonishing conservation of gene function across nearly a billion years. For instance, the same genes are involved in retinal development in humans and in fruit fly photoreceptors. And the BRCA1 gene that is mutated in breast cancer is responsible for repairing DNA breaks in plants and animals.

The genome of animals is also far more conserved than has been supposed. For instance, several colleagues and I recently demonstrated that animal chromosomes are 684 million years old.

It will be exciting, too, to explore the dark matter of the genome, and reveal how DNA sequences that dont encode proteins can still play a role in genome function and evolution.

Another important aim of the Earth Biogenome Project is conservation genomics. This field uses DNA sequencing to identify threatened species, which includes about 28 percent of the worlds complex organisms, helping us monitor their genetic health and advise on management.

Until recently, sequencing large genomes took years and many millions of dollars. But there have been tremendous technical advances that now make it possible to sequence and assemble large genomes for a few thousand dollars. The entire Earth Biogenome Project will cost less in todays dollars than the Human Genome Project, which was worth about US$3 billion in total.

In the past, researchers would have to identify the order of the four bases chemically on millions of tiny DNA fragments, then paste the entire sequence together again. Today they can register different bases based on their physical properties, or by binding each of the four bases to a different dye. New sequencing methods can scan long molecules of DNA that are tethered in tiny tubes, or squeezed through tiny holes in a membrane.

But why not save time and money by sequencing just key representative species?

Well, the whole point of the Earth Biogenome Project is to exploit the variation between species to make comparisons, and also to capture remarkable innovations in outliers.

There is also the fear of missing out. For instance, if we sequence only 69,999 of the 70,000 species of nematode, we might miss the one that could divulge the secrets of how nematodes can cause diseases in animals and plants.

There are currently 44 affiliated institutions in 22 countries working on the Earth Biogenome Project. There are also 49 affiliated projects, including enormous projects such as the California Conservation Genomics Project, the Bird 10,000 Genomes Project, and UKs Darwin Tree of Life Project, as well as many projects on particular groups such as bats and butterflies.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: paulbr75 / 2230 images

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Scientists Are Sequencing the Genome of Every Complex Species on Earth - Singularity Hub

Genome Editing Market: Snapshot

Genome editing tools have come a long way from the mid-twentieth century. In 1970s and 1980s, gene targeting was done using largely homologous combination, but was only possible in mice. Since then, the expanding science of genetic analysis and manipulation extended to all types of cells and organisms. Advent of new tools helped scientists achieve targeted DNA double-strand break (DSB) in the chromosome, and is a key pivot on which revenue generation in the genome editing market prospered. New directions for programmable genome editing emerged in the decades of the twenty-first century, expanding the arena.

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Cutting-edge platforms at various points in time continue to enrich genome editing market. Various classes of nucleases emerged, most notable of which is CRISPR-Cas. Research labs around the world have extensively used the platforms in making DSBs at any target of choice. Aside from this, agricultural sciences and medical sectors make substantial use of zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) in genome editing. Strides made in stem cell therapies, particularly in rectifying an aberrant mutation, have boosted the growth of the genome editing market. Genetic diseases such as muscular dystrophy and sickle cell disease present an incredible revenue prospect in the genome editing market. Ongoing research on novel vectors and non-vector approaches are expected to bolster the outlook of the market.

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Genomic editing refers to the strategies and techniques implemented for the modification of target genetic information of any living organism. Genome editing involves gene modification at specific areas through recombinant technology, which increases precision in insertion and decreases cell toxicity. Current advancement in genome editing is based on programmable nucleases. The genome editing market is presently witnessing significant growth due to increase in R&D expenditure, rise in government funding for genomic research, technological advancements, and growth in production of genetically modified crops. Companies have made significant investments in R&D in the past few years to develop cutting-edge technologies, such as, CRISPR and TALEN. For instance, Thermo Fisher Scientific is investing significantly in the development of its CRISPR technology for providing better efficiency and accuracy in research and also to fulfil the unmet demands in research and therapeutics. Cas9 protein and FokI protein have been combined to form a dimeric CRISPR/Cas9 RNA-guided FokI nucleases system, which is expected to have wide range of genome editing applications.

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The genome editing market is growing rapidly due to its application in a large number of areas, such as mutation, therapeutics, and agriculture biotechnology. Genome editing techniques offer large opportunities in crop improvement. However, the real potential of homologous recombination for crop improvement in targeted gene replacement therapy is yet to be realized. Homologous recombination is expected to be used as an effective methodology for crop improvement, which is not possible through transgene addition. Rise in the number of diseases and applications is likely to expand the scope of genome editing in the near future. It includes understanding the role of specific genes and processes of organ specific stem cells, such as, neural stem cells and spermatogonial stem cells. Genome editing has a significant scope to treat genetically affected cells, variety of cancers, and agents of infectious diseases such as viruses, bacteria, parasites, etc. However, genetic alteration of human germline for medicinal purpose has been debated for years. Ethical issues, comprising concern for animal welfare, can arise at all stages of generation and life span of genetically engineered animal.

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The global genome editing market can be segmented based on technology, application, end-user, and geography. In terms of technology, the genome editing market can be categorized into CRISPR, TALEN, ZFN, and other technologies. Bioinformatics has eased the process of data analysis through various technological applications. On the basis of application, the global genome editing market can be classified cell-line engineering, animal genome engineering, plant genome engineering, and others. Based on end-user, the genome editing market can be segmented into pharmaceutical and biotechnological companies and academic and clinical research organizations. In terms of region, the global genome editing market can be segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. North America is projected to continue its dominance in the global genome editing market owing to high government funding for research on genetic modification in the region. Asia Pacific is a rapidly growing genome editing market due to rise in investments by key players in the region. Rise in drug discovery and development activities, coupled with increasing government initiatives toward funding small and start-up companies in the biotechnology and life sciences industry, is a major factor expected to drive the genome editing market in North America during the forecast period. Players should invest in the emerging economies and the countries of Asia-Pacific like China, South Korea, Australia, India and Singapore in which the genome editing market is expected to grow at rapid pace in future, due to growing funding in research.

Key players operating in the global genome editing market are CRISPR Therapeutics, Thermo Fisher Scientific, GenScript Corporation, Merck KgaA, Sangamo Therapeutics, Inc., Horizon Discovery Group, Integrated DNA Technologies, New England Biolabs, OriGene Technologies, Lonza Group, and Editas Medicine.

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Genome Editing Market: Rise in drug discovery and development activities to drive the market - BioSpace

DUBLIN, Jan. 24, 2022 /PRNewswire/ -- The "Genomic Cancer Panel and Profiling Markets by Cancer, by Application, by Tissue and by Gene Type with Screening potential Market Size, Forecasting/Analysis, and Executive and Consultant Guides" report has been added to ResearchAndMarkets.com's offering.

This report provides data that analysts and planners can use. Hundreds of pages of information including a complete list of Current 2021 United States Medicare Fee Payment Schedules to help understand test pricing in detail. Forecast demand for new testing regimes or technologies. Make research investment decisions. Existing laboratories and hospitals can use the information directly to forecast and plan for clinical facilities growth.

Cancer Gene Panels and Genomic Profiling are quickly changing the diagnosis and treatment of cancers. The market is moving out of a specialized niche and going mainstream as Oncologists begin routinely using information on the hundreds of genes related to cancer. The market is exploding as physicians use all the information they can get in the battle against cancer.

While Pharmaceutical Companies see the potential to make nearly any therapy viable. The report has data on how test volumes have grown for the biggest players. Find out how this new way of understanding cancer will change cancer diagnostics forever.

Comprehensive panels, genomic profiling, high risk breast cancer panels. Learn all about how players are jockeying for position in a market that is being created from scratch. And some players are pulling way out in front and expanding globally. It is a dynamic market situation with enormous opportunity where the right diagnostic with the right support can command premium pricing. And the science is developing at the same time creating new opportunities with regularity. And the cost of sequencing continues to fall.

Key Topics Covered:

1 Market Guides1.1 Cancer Panel Market - Strategic Situation Analysis & COVID Update1.2 Large Comprehensive Cancer Panel Market - Situation Analysis1.3 Guide for Executives, Marketing, Sales and Business Development Staff1.4 Guide for Management Consultants and Investment Advisors1.5 Market Size and Shares - Large Comprehensive

2 Introduction and Market Definition2.1 What are Cancer Gene Panels and Profiling?2.2 The Sequencing Revolution2.3 Market Definition2.3.1 Revenue Market Size2.4 Methodology2.4.1 Authors2.4.2 Sources2.5 A Spending Perspective on Clinical Laboratory Testing2.5.1 An Historical Look at Clinical Testing

3 Market Overview3.1 Players in a Dynamic Market3.1.1 Academic Research Lab3.1.2 Diagnostic Test Developer3.1.3 Instrumentation Supplier3.1.4 Distributor and Reagent Supplier3.1.5 Independent Testing Lab3.1.6 Public National/regional lab3.1.7 Hospital lab3.1.8 Physician Office Labs3.1.9 Audit Body3.1.10 Certification Body3.2 Oncogenomics3.2.1 Carcinogenesis3.2.2 Chromosomes, Genes and Epigenetics3.2.2.1 Chromosomes3.2.2.2 Genes3.2.2.3 Epigenetics3.2.3 Cancer Genes3.2.4 Germline vs Somatic3.2.5 Gene Panels, Single Gene Assays and Multiplexing3.2.6 Genomic Profiling3.2.7 The Comprehensive Assay3.2.8 Changing Clinical Role3.2.9 The Cancer Screening Market Opportunity3.3 Cancer Management vs. Diagnosis3.3.1 The Role of Risk Assessment3.3.2 Diagnosis3.3.3 Managing3.3.4 Monitoring3.4 Phases of Adoption - Looking into The Future3.5 Structure of Industry Plays a Part3.5.1 Hospital Testing Share3.5.2 Economies of Scale3.5.2.1 Hospital vs. Central Lab3.5.3 Physician Office Lab's3.5.4 Physician's and POCT3.6 Currently Available Large Comprehensive Assays3.7 Pricing Profiling vs. Whole Exome (or Genome) Sequencing3.7.1 Medicare Profile Pricing3.7.2 Whole Exome Sequencing

4 Market Trends4.1 Factors Driving Growth4.1.1 Level of Care4.1.2 Companion Dx4.1.3 Immuno-oncology4.1.4 Liability4.1.5 Aging Population4.2 Factors Limiting Growth4.2.1 State of knowledge4.2.2 Genetic Blizzard4.2.3 Protocol Resistance4.2.4 Regulation and coverage4.3 Instrumentation and Automation4.3.1 Instruments Key to Market Share4.3.2 Bioinformatics Plays a Role4.4 Diagnostic Technology Development4.4.1 Next Generation Sequencing Fuels a Revolution4.4.2 Single Cell Genomics Changes the Picture4.4.3 Pharmacogenomics Blurs Diagnosis and Treatment4.4.4 CGES Testing, A Brave New World4.4.5 Biochips/Giant magneto resistance based assay

5 Cancer Panels & Profiles Recent Developments5.1 Recent Developments - Importance and How to Use This Section5.1.1 Importance of These Developments5.1.2 How to Use This Section5.2 Dante Labs Acquires Cambridge Cancer Genomics5.3 Celemics, Strand Partner on Integrated Platform for NGS Analysis5.4 Myriad Genetics Recalibrates Breast Cancer Panel for All Ancestries5.5 Burning Rock Revenues Rise5.6 Caris Life Sciences to Expand Liquid Biopsy Testing5.7 OncoDiag Announces Multiplex Test for Bladder Cancer Recurrence5.8 Intermountain and Myriad Combine Test Offering5.9 Illumina, Geneseeq to Offer Cancer Testing Kits in China5.10 Exact Sciences to Offer End-to-End Cancer Testing5.11 Guardant Health Turns to Tumor Tissue Sequencing5.12 Tempus Inks Oncology Testing Collaboration With Bayer5.13 Biocartis Collaborating With GeneproDx, Endpoint Health on Tests for Idylla Platform5.14 Wales to Routinely Screen Cancer Patients With Yourgene Elucigene Test5.15 Metastatic Cancer Markers Identified in Clinical WGS Study5.16 Stitch Bio Bets on CRISPR Tech5.17 Bayer, LifeLabs Launch Free NTRK Genetic Testing Program5.18 Foundation Medicine Liquid Biopsy Gets FDA Approval for Multiple Companion Dx5.19 Progress, Challenges in Liquid Biopsy Reimbursement5.20 Israeli Startup Curesponse Raises $6M5.21 Invitae, ArcherDX Merge to Advance Precision Oncology Offerings5.22 MD Anderson Precision Oncology Decision Support to Use Philips' Informatics Solution5.23 NeoGenomics, Lilly Oncology Partner for Thyroid Cancer Testing Program5.24 Germline Results Guides Precision Therapy in Advanced Cancer5.25 FDA Clears Cancer Genomic Profiling Kit From Personal Genome Diagnostics5.26 ArcherDX, Premier Collaborate to Evaluate Genomic Sequencing Assay for Cancers5.27 Labs Reporting Cancer Risk Mutations from Tumor Testing5.28 Users Begin Integrating Genomics Data for Clinical Decision Support5.29 Fujitsu Improves Efficiency in Cancer Genomic Medicine5.30 Thermo Fisher's automated sequencer to offer same-day, pan-cancer test results5.31 Universal Genetic Testing for All Breast Cancer Patients5.32 Exact Sciences buys Genomic Health5.33 Multi-Gene Liquid Biopsy Breast Cancer Panel5.34 Thrive to Develop Earlier Detection of Multiple Cancer Types5.35 New Gene Panel Identifies High Risk Prostate Cancer5.36 Guardant Health Liquid Biopsy Test to be Covered by EviCore5.37 Biocept Partnership Offering for Liquid Biopsy Adds Several Key Services5.38 Natera Commercializes Tumor Whole Exome Sequencing from Plasma5.39 Inivata Completes 39.8M Series B Funding Round5.40 Bio-Rad Clinical ddPCR Test, Diagnostic System Get FDA Clearance5.41 CellMax, Medigen Biotech Partner in Colorectal Cancer Clinical Trials5.42 Biodesix Acquires Integrated Diagnostics5.43 Predicine, Kintor Pharmaceuticals Partner on Clinical Trials, CDx

6 Profiles of Key Players6.1 10x Genomics, Inc6.2 Abbott Diagnostics6.3 AccuraGen Inc6.4 Adaptive Biotechnologies6.5 Aethlon Medical6.6 Agena Bioscience, Inc6.7 Agilent/Dako6.8 Anchor Dx6.9 ANGLE plc6.10 ApoCell, Inc.6.11 ArcherDx, Inc6.12 ARUP Laboratories6.13 Asuragen6.14 AVIVA Biosciences6.15 Baylor Miraca Genetics Laboratories6.16 Beckman Coulter Diagnostics6.17 Becton, Dickinson and Company6.18 BGI Genomics Co. Ltd6.19 Bioarray Genetics6.20 Biocartis6.21 Biocept, Inc6.22 Biodesix Inc6.23 BioFluidica6.24 BioGenex6.25 BioIVT6.26 Biolidics Ltd6.27 bioMerieux Diagnostics6.28 Bioneer Corporation6.29 Bio-Rad Laboratories, Inc6.30 Bio-Reference Laboratories6.31 Bio-Techne6.32 Bioview6.33 Bolidics6.34 Boreal Genomics6.35 Bristol-Myers Squibb6.36 Burning Rock6.37 Cancer Genetics6.38 Caris Molecular Diagnostics6.39 Castle Biosciences, Inc.6.40 Celemics6.41 CellMax Life6.42 Cepheid (Danaher)6.43 Charles River Laboratories6.44 Chronix Biomedical6.45 Circulogene6.46 Clinical Genomics6.47 Cynvenio6.48 Cytolumina Technologies Corp6.49 CytoTrack6.50 Datar Cancer Genetics Limited6.51 Diagnologix LLC6.52 Diasorin S.p.A6.53 Enzo Life Sciences, Inc6.54 Epic Sciences6.55 Epigenomics AG6.56 Eurofins Scientific6.57 Exact Sciences6.58 Exosome Diagnostics6.59 Exosome Sciences6.60 Fabric Genomics6.61 Fluidigm Corp6.62 Fluxion Biosciences6.63 Foundation Medicine6.64 Freenome6.65 FUJIFILM Wako Diagnostics6.66 GeneFirst Ltd.6.67 Genetron Holdings6.68 GenomOncology6.69 GILUPI Nanomedizin6.70 Grail, Inc.6.71 Guardant Health6.72 HalioDx6.73 HansaBiomed6.74 HeiScreen6.75 Helomics6.76 Horizon Discovery6.77 HTG Molecular Diagnostics6.78 iCellate6.79 Illumina6.80 Incell Dx6.81 Inivata6.82 Integrated Diagnostics6.83 Invitae Corporation6.84 Invivogen6.85 Invivoscribe6.86 Janssen Diagnostics6.87 MDNA Life SCIENCES, Inc6.88 MDx Health6.89 Menarini Silicon Biosystems6.90 Millipore Sigma6.91 Miltenyi Biotec6.92 MIODx6.93 miR Scientific6.94 Molecular MD6.95 MyCartis6.96 Myriad Genetics/Myriad RBM6.97 NantHealth, Inc.6.98 Natera6.99 NeoGenomics6.100 New Oncology6.101 NGeneBio6.102 Novogene Bioinformatics Technology Co., Ltd.6.103 Oncocyte6.104 OncoDNA6.105 Ortho Clinical Diagnostics6.106 Oxford Nanopore Technologies6.107 Panagene6.108 Perkin Elmer6.109 Personal Genome Diagnostics6.110 Personalis6.111 Precipio6.112 PrecisionMed6.113 Promega6.114 Qiagen Gmbh6.115 Rarecells SAS6.116 RareCyte6.117 Roche Molecular Diagnostics6.118 Screencell6.119 Sense Biodetection6.120 Serametrix6.121 Siemens Healthineers6.122 Silicon Biosystems6.123 simfo GmbH6.124 Singlera Genomics Inc6.125 Singulomics6.126 SkylineDx6.127 Stratos Genomics6.128 Sysmex Inostics6.129 Tempus Labs, Inc6.130 Thermo Fisher Scientific Inc6.131 Thrive Earlier Detection6.132 Todos Medical6.133 Trovagene6.134 Variantyx6.135 Volition6.136 Vortex Biosciences

7 The Global Market for Cancer Gene Panels and Profiles

8 Global Cancer Gene Panels & Profiles Markets - By Type of Cancer

9 Global Cancer Gene Panels & Profiles Markets - By Type of Application

10 Global Cancer Gene Panels & Profiles Markets - By Tissue Type

11 Global Cancer Gene Testing Markets - Germline and Somatic11.1 Global Market Somatic11.1.1 Table Somatic - by Country11.1.2 Chart - Somatic Testing Growth11.2 Global Market Germline11.2.1 Table Germline - by Country11.2.2 Chart - Germline Testing Growth

12 Potential Market Opportunity Sizes12.1 Potential Cancer Screening by Country: Lung, Breast & Colorectal12.2 Potential Cancer Screening by Country: Prostate, Other Cancer & All Cancer12.3 Potential Market Size - Cancer Diagnosis12.4 Potential Market Size - Therapy Selection

13 Appendices

For more information about this report visit https://www.researchandmarkets.com/r/qwgvdr

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Worldwide Genomic Cancer Panel and Profiling Industry to 2024 - Next Generation Sequencing Fuels a Revolution - PRNewswire

Is the pandemic finally looking to burn itself out? While the weeks trailing average of infections recorded daily has risen above 270,000, what stands out for its absence in Indias third wave is a fatality spike. Nearly three weeks in, our official 7-day curve of lives claimed daily by covid has stayed mostly flat under the 400 level. That this waves chief culprit Omicron sickens us far less than Delta, even as it spreads faster, would appear well borne out by the latest numbers. Reason enough to breathe easier and ease up on covid curbs? Not quite, some would argue. No one is safe until everyone is safe, we await confirmation that Omicron has achieved dominance, and as long as Delta lurks in the air, so do mortal risks. After being caught off-guard by last years horrid outbreak, which was identified as a Delta wave only after it had peaked, administrations across the country seem inclined not to take chances on safety this time. Yet, our lack of clarity on the mix of Sars-CoV-2 variants that Indians are exposed to speaks of yet another surveillance let-down. Unless Indian authorities have held genomic findings back, the scope for good data-driven decisions remains as narrow as before. In a country that boasts of sufficient diagnostic and statistical resources, this is odd indeed.

Our efforts at cracking viral gene codes have been tardy all along, but virus identification by genome sequencing was assumed to have got a major boost in late 2020, when India set up its genome consortium Insacog, with 38 government labs jointly charged with studying the pandemics genetic profile. In theory, representative samples drawn periodically from cases across the nation can offer a dynamic and therefore useful map of which strain is on the loose where. In reality, Insacogs reports on our variant break-up have lagged too far behind to be of policy-input help. Its website features monthly data. Omicron was found to be only a sliver last month; Delta, which made up the bulk of cases identified over May, June and July 2021, had been losing share but saw this trend reverse in November and December. As for the third wave that began this January, we remain mostly in the dark. All that has emerged so far is a stray remark on TV by the chief of our vaccine advisory panel more than a fortnight ago about three-fourths of all cases in Delhi, Mumbai and Kolkata being Omicron. Insacog, however, seems to be in silent mode, even as experts demand an update.

Insacogs website says it has sequenced about 91,300 genomes in allless than a tenth of the million-plus done by the update-happy UK that has far fewer people. Indias state-wise tallies show sharp variations, explained by big gaps in viral receipts, the inadequacy of which may have been the key problem. Unless data is valued equally by all stakeholders, it is hard to improve. Sadly, covid statistics remain a touchy topic in some parts, made touchier still by compensation claims having exceeded the official toll vastly in states like Gujarat and Telangana. In related news, Delhi has said it plans to identify the strains that had sickened the capitals deceased. This could reveal a bit about peoples health risk, but would hardly suffice. We also need to step up research on long covid, especially the bugs possible effects on organs other than our lungs. A preliminary paper published recently in the West reports some headway made on the brain fog that some patients experience. In India, wed be glad just to get a clear genomic snapshot.

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Wanted: A genomic map of our third covid wave - Mint