Category Archives: Genome

image:Wingless expression (red) in regenerating (left), developing (center) and tumorigenic (right) wing primordia of Drosophila. view more

Credit: IRB Barcelona

Barcelona, 22 August, 2021 The first mutation of the wingless gene was found by accident in Drosophila in the 1970s, following the observation of flies that did not possess wings, hence its name. Fifteen years after its discovery, the gene was found to be conserved in mammals, an event that gave rise to foundation of the wnt gene family. Mutations in wnt genes lead to various types of cancer.

The wnt gene family, including its founding member, the wingless gene, regulates several processes during the embryonic development of both insects and mammals. However, if this is true, then why did the first mutation of the wingless gene discovered only affect the wings of Drosophila flies? This was the question put forward by the IRB Barcelona Development and Growth Control lab.

Using gene editing techniques, such as CRISPR/Cas9, the researchers discovered an evolutionarily-conserved genomic region that regulates the expression of the Wingless protein only during the formation of the wing. Using functional assays, the scientists discovered that this regulatory region not only acts to exclusively promote wing formation but it also regenerates the wings when damaged.

Ensuring wing formation in different ways

The researchers showed that this regulatory region is exclusively involved in the regulation of Wingless expression during the formation of the wing. Their functional assays also discovered the presence of two highly redundant modules in this regulatory region that are activated by independent signalling pathways.

"What we have discovered in this study is a highly robust genetic regulation mechanism that ensures proper wing development, and this mechanism is consistent with the crucial importance that these structures have for insects in general", stated Dr. Marco Milan, an ICREA researcher and the Head of the Development and Growth Control Laboratory, who led this study. "Wing development was an enormous evolutionary advantage for insects and it is what permitted their expansion and diversification," Dr. Miln added.

Regeneration and tumours

When organ damage occurs, the injured cells send signals to their surrounding cells so that these then divide in order to restore the organ. The authors of this investigation have shown that Wingless is also the molecule responsible for signalling healthy cells to divide and regenerate tissue, and that the regulatory region involved in wing formation is also activated in situations of damage in order to induce the expression of Wingless.

The research team demonstrated in functional assays that the JNK stress signalling pathway acts in a redundant manner on the two existing modules. "Once again, a very robust genetic regulatory mechanism ensures not only the correct development of the wing but also its ability to regenerate", stated Elena Gracia-Latorre and Lidia Prez, the initial research authors.

As a final note, the researchers performed experiments in which they blocked the damaged-cell removal process, to find that the Wingless regulatory zone remained continually activated. Due to the constant presence of Wingless, the cells proliferated uncontrollably, and this eventually gave rise to the formation of tumorous and malignant growths. "This allows us to propose that regeneration and tumour development are two sides of the same coin: if Wingless is induced for a short period of time, it forms the wing normally or allows it to regenerate, but if it is maintained chronically, then it causes overgrowth and a tumour, concludes Dr. Milan.

Nature Communications

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A genomic region that is exclusively dedicated to the formation and regeneration of a single organ - EurekAlert

CRISPR is a family of DNA sequences found in the genomes of prokaryotic organisms such as bacteria and archaea. These sequences are derived from DNA fragments of viruses called bacteriophages that had previously infected the prokaryote. They are used to detect and destroy DNA from similar bacteriophages during subsequent infections, providing the prokaryote with a sort of immunity.

The following is an interview with CRISPR co-discoverer and Nobel Prize-winner Dr. Jennifer Doudna.

Describe the eureka moment around CRISPR the moment when you realized that this technology was not only possible but actually worked. How did you feel? Has your feeling changed since that eureka moment? If so, how?

Theres one moment that stands out in my mind, right at the time we realized what CRISPR could do and that we could reprogram it to edit specific sequences of DNA. I was cooking dinner and thinking about it, and I burst out laughing. My son was in the kitchen and he asked why I was laughing. So I explained it to him with a little drawing of a car zooming around, grabbing onto viruses, and chopping them up. I think my drawing did the trick, because he started laughing too.

The implications of this finding were too big to understand all at once. Its been ten years since that time now, and everything that has happened since surpassed any expectations I had back then. With multiple therapies in clinical trials, plants in fields that help farmers adapt to a changing climate, and countless uses of CRISPR in life science research, the scope of what has been achieved in just ten years continues to surprise me.

What excites and inspires you most about the possibilities of CRISPR technologies?

I recently spoke with Victoria Gray, one of the first people to receive a CRISPR-based therapy for sickle cell disease. Hearing from her about how her life has changed for the better, how shes no longer in constant pain and able to go back to work and spend more time with her family theres nothing more inspiring than real human impact. Thats what drives the work we do at the institute that I started at UC-Berkeley, the Innovative Genomics Institute (IGI), where the focus is not just developing new therapies and agricultural products, but making sure they reach the people who need them most.

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What is the most interesting, or counterintuitive, use of CRISPR technology that youve encountered thus far?

We talk a lot about the ability of CRISPR to cut DNA, but its ability to find a specific sequence of DNA is just as interesting. Thats not an easy thing to do, and it turns out that it can be really useful in other ways. For example, at the IGI, were developing CRISPR-based diagnostics for infectious disease. Instead of editing DNA, these tests quickly find a specific sequence of DNA from a pathogen, like the SARS-CoV-2 virus or HIV, and then release a fluorescent marker. The great thing about these tests is that theyre fast, can be performed anywhere, and should be quite cheap to produce. After everything weve all experienced during the pandemic, its clear that rapid point-of-need tests are going to be increasingly important.

Are there any parallels in history of a technology that fundamentally changed human life?

In many ways CRISPR genome editing builds on groundbreaking technologies and innovations that came before it, and each one was a watershed moment for science. We needed X-ray crystallography to understand the structure of DNA, Sanger sequencing to be able to read it, PCR to make copies of it, and the Human Genome Project and other large bioinformatics projects to start to understand the bigger picture of how genomes function. Being able to edit the genome is the next chapter in this story, but it couldnt exist without the others that came before it.

How can we most responsibly use the power this technology has unlocked? Where should we put the guardrails?

With any powerful technology, there is always potential for its misuse. And we have already seen this, even though the vast majority of scientists are using it responsibly. Determining what constitutes misuse, what is unethical, what is medically necessary that is where a lot of the discussion is focused at the moment. There is broad agreement on certain topics, particularly around human germline editing, but when it comes to questions of ethics, there will always be gray areas.

One risk that is often overlooked is the real possibility that some of the advances we make in genome editing will benefit a small fraction of society. With new technologies this is often the case at first, so we have to consciously work from the start to make new cures and agricultural tools that are accessible and affordable.

In your mind, what does it mean for humanity to have the ability to directly alter genetic material so precisely?

Its a powerful tool, and one that can be used to do a lot of good. Sickle cell disease affects millions of people worldwide, and its caused by a single-letter mutation in just one gene. This has been understood for a long time, but we didnt have the means to fix that mutation. There are several thousand other genetic diseases, including very rare diseases that are often neglected, that we can now look to address. It goes beyond medicine: Climate change is impacting agriculture, and agriculture itself is contributing to climate change. With genome editing, we can mitigate both of those impacts.

How do you think CRISPR will affect our understanding and definition of what it means to be human?

Understanding even just a little bit about genetic disorders what causes them, how many people are affected by them increases your compassion for what people are going through of no fault of their own. You also start to understand that there are people who have genetic mutations that affect their lives, but dont necessarily view them as diseases or problems to fix. CRISPR itself may not change what it means to be human, but perhaps having a tool that can rewrite our DNA helps to shine a light on all of the diversity that humanity already encompasses.

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10 years on, a spin-off use for CRISPR: Infectious disease testing - Big Think

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Published 13 hours ago

Submitted by Illumina

Originally published on Illumina News Center

SAN DIEGO, August 22, 2022/CSRwire/ -- Illumina, Inc. (NASDAQ: ILMN), a global leader in DNA sequencing and array-based technologies, announced that on September 30, its Illumina Genomics Forum (IGF) will feature Bill Gates, co-chair of the Bill & Melinda Gates Foundation, who will deliver a keynote address on the remarkable potential of genomics to change the trajectory of global health. In addition, IGF will host a panel session titled "Making 'Genomics for All' More than a Mantra," on the requirements needed to ensure broader access to genomic health.

"Genomics should be available to the many, not the few, and even though the genomic health era has already led to breakthrough discoveries that are advancing medical care, the benefits have not yet had a true globalimpact," said Kathryne Reeves, chief marketing officer for Illumina. "Through sessions led by Bill Gates and expert panelists, Illumina Genomics Forum will help attendees see and understand the path toward global health equity."

The "Genomics for All" panel includes representatives driving increased access to genomic health, including:

Illumina previously announced that former U.S. President Barack Obama will headline the inaugural forum in a fireside chat on the evening of Wednesday, September 28. Twelve years after the passage of the Affordable Care Act, Obama will discuss the continued need for equity, accessibility, and smarter healthcare to improve the human condition. Additional speakers, panels, and details about the event agenda will continue to be released in the coming weeks.

Other IGF key themes include:

IGF will take place in San Diego from September 28 through October 1. For more information and to register for the conference, go to illuminagenomicsforum.com.

About Illumina

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

Investors:Salli Schwartz858-291-6421IR@illumina.com

Media:Adi RavalUS: 202-629-8172PR@illumina.com

SOURCE Illumina, Inc.

Illumina is improving human health by unlocking the power of the genome. Our focus on innovation has established us as the global leader in DNA sequencing and array-based technologies, serving customers in the research, clinical, and applied markets. Our products are used for applications in the life sciences, oncology, reproductive health, agriculture, and other emerging segments.

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RNA-seq read mapping in wheat and Pst reflects the susceptibility of the interaction

We selected three bread wheat varieties (Oakley, Solstice and Santiago) previously demonstrated to display different susceptibility levels to our two selected Pst isolates (F22 and 13/14)14. We quantified visible phenotypes of pathogen infection and infection types (ITs) at 12 days post-inoculation (dpi) following the 04 scale16 (Supplementary TableS1). Oakley was fully susceptible to both Pst isolates, while Solstice was moderately susceptible to Pst isolate F22 and almost fully susceptible to Pst isolate 13/14; Santiago was resistant to Pst isolate F22 and showed moderate resistance to Pst isolate 13/14. These results confirmed the range of susceptibility/resistance exhibited by the selected wheat varieties for this study. We infected each of the three wheat varieties with each of the two Pst isolates individually (Fig.1a) and collected samples at 1, 3, 7 and 11 dpi for RNA-seq analysis, alongside mock-inoculated samples from each variety collected at 12h post-inoculation (hpi). Following quality filtering, we aligned clean reads from each of the 81 generated samples to the wheat reference genome (Refseq v1.1)17 and Pst reference genome (isolate Pst-104E18).

a Diagram of the stages of Pst development during plant infection. The time points selected for RNA-seq analyses (1, 3, 7 and 11 days post-inoculation [dpi]) are highlighted. S uredinospore, SV substomatal vesicle, IH invasive hyphae, HM haustorial mother cell, H haustorium, P pustule, G guard cell. Inspired by a schematic illustration from61. b Percentage of reads mapping to the wheat or Pst reference genomes across wheat varieties and pathogen isolates. Following quality filtering, RNA-seq reads were mapped to the Pst reference genome (isolate Pst-104E18) and wheat reference genome Refseq v1.117. Values represent an average of three independent biological replicates (independent infected plants) for each Pstvariety pair. c Principal component analysis (PCA) of wheat gene expression profiles shows that samples from all Pstvariety pairs cluster into two well-defined groups: 1) 1 dpi; and 2) all remaining time points. d Independent PCA on 1 dpi samples only (left) or remaining time points (right) illustrating the clustering of 1 dpi samples by host variety, for infection by Pst isolate F22. e Differentially expressed genes (DEGs) are more numerous at 1 dpi, with samples infected with Pst isolate 13/14 showing more isolate-specific DEGs than those infected with Pst isolate F22. The number of DEGs was defined at each time point by comparing normalised transcript abundance for each Pst-wheat interaction against the corresponding mock-inoculated control using a negative binomial regression (Wald test) in DESeq2. Genes were considered differentially expressed when q-value<0.05.

We detected similar proportions of reads mapping to the wheat and Pst reference genomes across samples collected at 1 and 3 dpi (average of 85.51.5% for wheat and<1% for Pst, Fig.1b). By 7 dpi, the percentage of reads mapping to the wheat and Pst genomes varied and reflected the degree of susceptibility between the respective varietypathogen pairs. We observed the largest differences between varieties at 11 dpi upon infection with Pst isolate F22. Indeed, while we obtained an average of 45.724.0% reads mapping to wheat and 18.711.5% to Pst for the most susceptible interaction (Pst isolate F22Oakley), the fraction of reads mapping to Pst decreased with higher host resistance. The moderately susceptible interaction (Pst isolate F22Solstice) returned 73.620% of reads mapping to wheat and 5.768.02% to Pst, compared to 87.00.92% of reads mapping to the wheat genome and 0.050.02% to Pst in the context of the most resistant interaction (Pst isolate F22Santiago). Notably, the percentages of reads mapping to the wheat genome were comparable for the SantiagoPst isolate F22 pair between early and later time points, as well as with mock-inoculated samples (87.31.8%), in agreement with the high resistance of the host to the pathogen (Fig.1b). By contrast, infection of all three varieties with Pst isolate 13/14 resulted in similar percentages of reads mapping to each reference genome (host and pathogen) at 7 and 11 dpi, although samples collected from the highly susceptible variety Solstice showed the largest percentage of reads mapping to Pst at 11 dpi relative to the other two varieties (Fig.1b). This analysis illustrates that the percentage of reads mapping to the wheat and Pst genomes at later time points reflect the degree of susceptibility of each Pstvariety interaction.

To assess the host response to Pst infection under different levels of susceptibility, we determined wheat transcript abundances at each time point for each Pstvariety interaction. We normalised our data to account for library size and samples with low read counts before conducting a principal component analysis (PCA). We generated scatterplots of the first two principal components for each Pst isolate, which identified two well-defined groups across all Pst-infected samples: (1) samples collected at 1 dpi and (2) samples collected at all remaining time points (Fig.1c). As samples collected at 1 dpi clustered separately from all others and might obscure later transcriptome patterns, we repeated the PCA by separating the 1 dpi samples from the others (Fig.1d and Supplementary Fig.S1). The scatterplot of the first two principal components for all 1 dpi samples demonstrated a clear separation by Pst isolate and wheat variety. We also noticed that separation between wheat varieties tends to follow their genetic relatedness, with Santiago grouping closely with its parent variety Oakley, whereas the unrelated Solstice variety clustered separately (Fig.1d and Supplementary Fig.S1). Analyses of the remaining time points showed a similar distribution for both Pst isolates, with mock-inoculated control samples clustering together and away from the remaining time points (3, 7 and 11 dpi). These results suggest that host transcript abundance is largely similar at 3 dpi onward irrespective of the Pst isolate or the level of susceptibility of the wheat variety used for infection.

We identified differentially expressed genes (DEGs) at the different time points by comparing normalised transcript abundance for each Pstvariety interaction against their respective mock-inoculated controls. Overall, we observed substantial overlap between DEGs from different Pstvariety pairs, ranging from 68.713.0% (standard deviation) to 59.514.2% shared between Pst F22- and Pst 13/14-infected samples. In agreement with the PCA, we detected far more DEGs at 1 dpi (q-value<0.05), with an average number of 27,9735453 DEGs across all Pstvariety interactions (Fig.1e), compared to 91251193 at 3 dpi, 13,3573305 at 7 dpi, and 13,9285222 at 11 dpi. Looking at Pst isolate-specific transcriptional responses, we determined that all wheat varieties exhibit more DEGs specific for Pst 13/14 infection than with Pst F22 (Fig.1e and Supplementary Data1 and 2). This pattern was particularly evident at 1 dpi, with 30,7331886 DEGs across the three varieties infected with Pst 13/14, of which 10,0355825 were unique to Pst 13/14. Conversely, across the three varieties infected with Pst F22 a total of 25,2136923 DEGs were identified, of which 4516 (range 9339987) were specific to Pst F22 at 1 dpi. Notably, 96.6% of all DEGs at 1 dpi in Santiago plants infected with Pst F22 were also differentially expressed in Santiago infected with Pst 13/14, despite the difference in susceptibility (resistance for Pst F22, moderately resistant for Pst 13/14).

To identify biological processes associated with variety-specific expression profiles in response to Pst infection, we generated functional enrichment networks for each Pstvariety pair (Fig.2a and Supplementary Figs.S2 and S3). Accordingly, we assigned gene ontology (GO) terms to all DEGs where possible and identified those significantly enriched in each condition (q-value>0.0005). We detected enrichment for second-level GO terms across all conditions and time points that reflected general responses to Pst infection and included GO:0009536 (plastid), GO:0009507 (chloroplast) and GO:0003824 (catalytic activity) (Fig.2a and Figs.S2 and S3). Focusing on DEGs at 1 dpi, all Pstvariety pairs showed enrichment in functions related to response to biotic stimulus, chloroplast and photosynthesis, metal binding (ironsulfur cluster binding), cell redox homoeostasis and cell metabolism, including transferase activity, hydrolase activity and phosphatase activity (Fig.2a). Looking across all wheat varieties, we identified 1494 DEGs specifically in response to infection with Pst F22 and another 8627 DEGs specific to inoculation with Pst 13/14 (Fig.2b). Functional annotation of each set of DEGs highlighted functions related to protein transport and protein localisation for those specific to Pst F22 infection (Fig. S4), while those specific to Pst 13/14 infection were related to part of the chloroplast, the chloroplast membrane and photosystems (Fig.2c).

a Functional enrichment network for each Pstvariety pair identified in samples taken at 1 dpi. Gene ontology (GO) terms were assigned to all DEGs where possible and those identified as significantly enriched (q-value<0.0005) in at least one Pst-varietal pair are represented by a node, with node sizes proportional to the number of genes annotated with the GO term. Edges indicate overlapping member genes and conservation of GO term enrichment is highlighted by node border colour. Highly similar gene sets formed clusters, which were annotated and labelled with appropriate summarising terms. b Venn diagram illustrating the extent of overlap between the number of DEGs conserved for the three wheat varieties at 1 dpi upon inoculation with Pst isolate 13/14 or Pst isolate F22. c Functional GO term enrichment analysis results for the 8627 Pst 13/14-specific DEGs. GO terms were annotated when Log(q-value)>20 (first panel) or Log(q-value)>15 (second panel). Circle size represents the number of genes annotated within the particular enriched function; circle colour represents the GO term classification: molecular function (MF, blue), biological process (BP, pink) and cellular component (CC, green).

We hypothesised that the greater number of DEGs shared across wheat varieties infected with Pst 13/14 reflects either the more homogeneous susceptible phenotypes or the stronger transcriptional reprogramming induced by this isolate. To explore this question in more detail, we built co-expression clusters for each Pstvariety pair by using the 8,627 DEGs identified at 1 dpi (Figs. S5S10). We classified the clusters into two classes based on expression profiles: (1) early upregulated clusters whose constituent genes were highly expressed at 1 dpi but returned to mock-inoculated levels by 3 dpi and (2) early downregulated clusters whose genes were expressed at lower levels than the controls at 1 dpi but returned to mock-inoculated levels by 3 dpi. For example, during the fully susceptible interaction between Santiago and Pst 13/14, we classified 2127 genes across two co-expression clusters as early upregulated and 2318 genes from two co-expression clusters as early downregulated. Using the same method in the context of the resistant interaction between Santiago and Pst F22, we identified 1826 genes across three co-expression clusters as early upregulated and 2069 genes from one co-expression cluster as early downregulated (Fig.3a).

a Example of co-expression clusters classified as containing early upregulated (red) or early downregulated (blue) genes following infection of Santiago with Pst isolates 13/14 and F22. Co-expression clusters were generated using the 8627 Pst 13/14-specific DEGs. The coloured line represents the average normalised expression of all genes in a given co-expression cluster. b, c GO terms for functionally enriched biological processes across the co-expression clusters from 8627 Pst 13/14-specific DEGs, assessed for each Pstvariety pair, and classified as early upregulated (b) or early downregulated (c) genes. Significant Log(q-value) values are represented using a 0100 scale and GO terms with Log(q-value)>5 are shown.

GO term enrichment analysis indicated that early upregulated DEGs are associated with a diverse array of cellular processes (Figs.3b and S11). All co-expression clusters for each of the three varieties infected with Pst 13/14 contained genes mainly involved in the myosin complex and peroxisomes. The resistant interaction (Pst isolate F22Santiago) was the only one associated with the NatA acetyltransferase complex, which also contained genes involved in protein deubiquitination. In terms of biological processes, early upregulated genes in the context of resistant and moderately susceptible interactions included mRNA metabolism and protein modification by small protein conjugation or removal. Susceptible interactions comprised genes involved in organelle organisation, protein transport, RNA processing, protein modification and pyridine nucleotide salvage. By contrast, we observed shared functions across all conditions for genes classified as early downregulated (Figs.3c and S12). In terms of cellular components, these co-expression clusters included genes annotated as part of the chloroplast. In agreement with this observation, photosynthesis was the main biological process enriched in all clusters, with other enriched processes such as organonitrogen compound biosynthesis, peptide metabolism and translation. Notably, the specific early downregulated genes associated with the chloroplast and involved in photosynthesis differed between each Pstvariety pair.

Among the DEGs at 1 dpi, we observed an enrichment for functions associated with defence-related responses. We selected genes participating in programmed cell death (48 genes), response to salicylic acid (SA; 59 genes), the innate immune response (179 genes), defence response to fungi (151 genes) and those predicted to encode nucleotide-binding site leucine-rich repeat (NLR)-type R proteins (9078 genes) for further analysis. We normalised their expression and determined the median value (Fig.4a, b). Most varieties exhibited a consistent upregulation of transcript levels across all categories at 1 dpi, followed by a drop in expression at 3 dpi and a later increase at 7 and 11 dpi. Importantly, the expression of genes belonging to all four defence-related response processes reaches a higher peak at later stages of infection (711 dpi) in the resistant interaction (Pst isolate F22Santiago) relative to its susceptible counterpart (Pst isolate 13/14Santiago) (Fig.4a). Turning to genes annotated as encoding potential NLRs, we detected most DEGs from this class at 1 dpi. At this time point, we identified the greatest numbers of NLR DEGs for Oakley infected with Pst 13/14 (most susceptible interaction), followed by Solstice infected with Pst 13/14 (fully susceptible) and Pst F22 (moderate susceptibility). The lowest numbers of NLR DEGs were for Santiago infected with Pst F22 (resistant interaction) and Oakley infected with Pst F22 (fully susceptible) (Fig.4b). However, we noted that at 1 dpi in Oakley infected with Pst F22, many genes involved in defence-related responses lacked the expression peak seen in other Pstvariety pairs, likely due to the peak occurring outside of the sampling timepoint in this case. Overall, our results suggest that the outcome of the hostpathogen interaction may be decided early during initial fungal colonisation.

a Median expression of normalised transcripts per million (tpm) values obtained for genes annotated as being involved in response to salicylic acid (GO:0009751), defence response to fungus (GO:0050832), innate immune response (GO:0045087) and cell death (GO:0012501). The peak in gene expression at later stages of infection (711 dpi) is more pronounced in resistant interactions (Pst isolate F22Santiago) when compared to its susceptible counterpart (Pst isolate 13/14Santiago). b The number of DEGs encoding proteins with typical NLR domains is greatest at 1 dpi, with the most DEGs at this time point identified in samples from Oakley infected with Pst 13/14 (most susceptible interaction). Typical NLR domains were defined as IPR001611:Leu-rich_rpt, IPR032675:LRR_dom_sf, IPR002182:NB-ARC, IPR027417:P-loop_NTPase. Genes were considered differentially expressed compared to the control when q-value<0.05.

Among the early downregulated genes, we noticed the presence of many genes encoding proteins with GO terms associated with the chloroplast (Fig.3c). We identified components of photosystem I (Psah2) and II (PsbQ proteins and PsbO2), enzymes from the CalvinBensonBassham cycle (pyruvate kinase [PRK], Ribose-5-phosphate isomerase [RPI], Rubisco, Fructose-bisphosphate aldolase [FBA1]), chloroplast calcium signalling components (CAS), proteins involved in chloroplast RNA metabolism (CSP41a and CSP41b) and isochorismate synthase 1 (ICS1) that synthesises SA in the chloroplasts from chorismic acid (Fig.5a). In each case, their gene expression was downregulated at 1 dpi, followed by a sharp peak in expression at 3 dpi and a second rapid decline by 7 dpi. The most resistant interaction (Pst isolate F22Santiago) was the notable sole exception across Pstvariety pairs, as the expression of many of these genes, failed to decline or declined to a lesser extent after 3 dpi than with more susceptible interactions (Supplementary Fig.S13).

a Schematic illustration of the chloroplast. The genes encoding the proteins marked with a star were identified as differentially expressed at 1 dpi across wheat varieties upon infection with Pst isolate 13/14. b Many genes are annotated with chloroplast-related functions among the 8627 Pst 13/14-specific DEGs, as 1038 DEGs belong to eight second-level GO terms with chloroplast-related functions. c Chloroplast-related DEGs show a conserved, temporally regulated expression profile during Pst infection. Normalised transcripts per million (tpm) values were used to determine the median expression levels for genes assigned to each of the eight chloroplast-related GO terms.

We explored the expression patterns of these nuclear genes encoding chloroplast-localised proteins (NGCPs) during a susceptible Pst-wheat interaction by re-examining the enriched GO terms among the 8627 Pst 13/14-specific DEGs. We obtained 1038 DEGs that belong to eight second-level GO terms with chloroplast-related functions. For each of the eight categories, we determined the genome-wide number of genes associated with each GO term, which illustrated the high proportion of chloroplast-related genes among the 8627 DEGs (26.685.7% for each GO term) (Fig.5b and Supplementary Data3). In addition, all chloroplast-related genes followed the same pattern of expression observed above, with a sharp increase in expression at 3 dpi, followed by a rapid decline by 7 dpi, except in the highly resistant interaction (Pst isolate F22Santiago; Fig.5c). This conserved gene expression profile likely reflects a well-coordinated transcriptional modulation of genes encoding chloroplast-targeted proteins upon pathogen recognition.

We selected the putative chloroplast-localised stem-loop RNA binding protein TaCSP41a among NGCPs for detailed analyses. TaCSP41a was selected due to the availability of tetraploid Kronos TILLING mutants and as CSP41 abundance has previously been linked to abiotic stress in Arabidopsis and tomato (Solanum lycopersicum)19,20. To investigate the expression pattern of CSP41 in more detail in response to biotic stress, we performed an RT-qPCR analysis of TaCSP41a transcript levels at 12 hpi, 2, 5, 9 and 11 dpi following infection of the wheat varieties Oakley, Santiago and Solstice with Pst F22. We designed primers to amplify all three TaCSP41a homoeologues simultaneously and compared expression levels between infected and mock-inoculated plants (Fig.6a). TaCSP41a was substantially more highly expressed at 12 hpi in the highly susceptible variety Oakley and expressed significantly lower levels in the highly resistant variety Santiago upon infection (Fig.6a). In all susceptible interactions, TaCSP41a was initially more highly expressed before decreasing substantially, reaching its lowest levels by 5 dpi for infected Oakley and 2 dpi for infected Solstice. These observations confirmed a link between TaCSP41a expression early during infection and the extent of susceptibility to Pst infection as shown in the RNA-seq analyses.

a TaCSP41a expression during a controlled infection time course of the wheat varieties Oakley, Solstice and Santiago with Pst isolate F22. Relative TaCSP41a expression was measured by RT-qPCR from all three homoeologous copies simultaneously and compared to mock-inoculated control plants, with the UBC4 gene used as a reference53. Two independent leaves from the same plant were pooled and three independent plants were analysed for TaCSP41a expression at each time point. Asterisks denote statistically significant differences (***p<0.005, **p<0.01, *p<0.05; 2-tailed t-test). b TaCSP41a-A co-localises with chlorophyll autofluorescence. TaCSP41a-A-GFP was transiently expressed in N. benthamiana and images were captured after 2 days. Images are representative of >10 images captured, all displaying co-localisation of TaCSP41a-A-GFP and chlorophyll autofluorescence. Left, individual TaCSP41a-A-GFP (top) and chlorophyll autofluorescence (bottom) patterns; right, merged image of TaCSP41a-A-GFP and chlorophyll autofluorescence illustrating co-localisation. Scale bars, 10m.

To test the subcellular location of TaCSP41a, we scanned the predicted protein sequence of the three homoeologues TaCSP41a-A, TaCSP41a-B, TaCSP41a-D for potential targeting signals. We detected a chloroplast targeting peptide with a high probability (>99%) in all three homoeologues (Supplementary TableS2). Encouraged by this result, we generated a fusion construct by cloning the TaCSP41a-A coding sequence in-frame and upstream of that of the green fluorescent protein (GFP) and transiently infiltrated the resulting TaCSP41a-A-GFP construct in Nicotiana benthamiana leaves. We observed GFP fluorescence in foci that co-localise with chlorophyll autofluorescence, as determined by confocal microscopy, supporting the notion that TaCSP41a is a chloroplast-resident protein (Fig.6b).

To assess the contribution of TaCSP41a to Pst-induced disease progression, we looked for tetraploid Kronos TILLING mutants21. We identified two mutant lines (Kronos3238 and Kronos3239) introducing early stop codons in the TaCSP41a-A sequence at amino acids 218 and 174 (Supplementary Fig.S14). We obtained homozygous TILLING mutant lines by self-pollination. We infected F2 homozygous progeny (TaCSP41a-AF218* and TaCSP41a-AQ174*) with Pst 13/14 and compared their disease phenotypes to the wild type (WT, cv. Kronos) and a Kronos3238 sibling carrying the wild-type allele at TaCSP41a-A (Fig.7a). Both mutant lines displayed limited sporulation and higher Pst resistance at 20 dpi, with a substantial reduction in the extent of leaf area infected by Pst, compared to both the Kronos WT and the wild-type Kronos3238 sibling (Fig.7b). Leaves of the TaCSP41a-AF218* and TaCSP41a-AQ174* mutant lines remained largely green outside of a few necrotic spots consistent with localised programmed cell death. By contrast, both WT lines were uniformly chlorotic, with low or no necrotic lesions (Fig.7a). The TaCSP41a-AQ174* mutant line displayed a stronger phenotype, with no chlorosis and only small necrotic regions in all plants tested. Together, these results demonstrate that disrupting TaCSP41a-A function promotes tolerance to Pst 13/14, indicating a role for TaCSP41a in supporting Pst disease progression.

a TaCSP41a-AF218* and TaCSP41a-AQ174* disruption mutants are more resistant to infection by Pst isolate 13/14 compared to the Kronos wild type (WT) or the Kronos ethyl methanesulfonate (EMS) mutant Kronos3238 carrying a WT allele at TaCSP41a. Images were captured at 20 dpi. b Lower rates of leaf infection in the TaCSP41a-A disruption mutants at 20 dpi, represented as box and whiskers plots. Lowercase letters denote statistically significant differences by Duncans multi-range test (p<0.05). Horizontal bars, median values; boxes, upper (Q3) and lower (Q1) quartiles; whiskers, 1.5the inter-quartile range.

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Temporally coordinated expression of nuclear genes encoding chloroplast proteins in wheat promotes Puccinia striiformis f. sp. tritici infection |...

Ultima CEO Gilad Almogy, Ph.D./courtesy of Ultima Genomics

The information stored within the confines of the human genome is some of the most important data we can use in the diagnosis of disease, prevention efforts and therapeutics. Despite the fact that the technology to conduct whole genome sequencing (WGS) has been around for decades, financial barriers have stood in the way not just for patients and doctors looking for information, but for researchers as well.

Leveraging over $600 million in funding and five years of hard work, Newark, California-based Ultima Genomics has designed a way to surmount that financial barrier lowering the cost of a human genome from the realm of $1,000 to just $100.

Ultima has achieved this through the use of its sequencing architecture which replaces the traditional flow cell, a channel that contains all of the surfaces where chemistry and imaging occur during sequencing, with a silicon wafer. The technology serves the same function but at a lower cost with larger surface area, allowing for billions of reads. The process is easier to scale, amounting in large volumes of genetic data, and avoids costly and complicated fluidics.

The company also touts novel scalable chemistry, which combines the speed, efficiency and read lengths of natural nucleotides with the accuracy and scalability of endpoint detection. Add machine learning at the genome scale that can deliver accurate results and youve got yourself a cost-effective and useful human genome ready for interpretation.

Why it Matters

The importance of cost goes beyond simply enabling access to a larger quantity of existing genomic solutions. It also enables qualitatively different experiments to be envisioned and executed, not just once, but routinely, Ultima CEO Gilad Almogy, Ph.D. said in an interview with BioSpace. This can enable scientists to ask new questions they previously couldnt answer, and it can change the way genomic information is incorporated into the broader healthcare system.

The $100 genome stands to make genomics research that was once thought of as impossible, possible. In 2020, an article celebrating the 20th anniversary of Nature Reviews Genetics discussed the future of genetics and genomics research. In the piece, Stacey Gabriel, senior director of the genomics platform at the Broad Institute, commented that the real promise of genome sequencing lies in true population-scale sequencing at the scale of tens of millions of individuals that would enable the comprehensive, unbiased study of the human genome and the variations found within it.

Genomic research has provided physicians with a wealth of knowledge about genes that can increase a persons risk of developing a certain condition, such as the BRCA2 gene which is linked to an increased risk of developing breast and ovarian cancer. However, without the ability to conduct large-scale studies, simply understanding the role that one gene or a handful of genetic mutations plays in developing disease is often not enough information to elucidate the genome's full impact. With scalable and cost-effective WGS, it will become much easier for researchers to understand the parties within our genome that contribute to the manifestation of disease, which could ultimately lead to targeted therapeutics.

Genomic Data can Inform Treatment

Gabriel stated that she believes WGS should become a part of the electronic health record. There are plenty of good reasons to collect and include genomic data as it relates to health and disease. Beyond using this data to understand the risk someone is at for a certain condition, genomic information can help direct treatment. For example, some cancer therapies specifically target genetic mutations or alterations that have occurred in the tumor microenvironment. If patients and physicians have access to more affordable genomic testing, they can use the information to choose a targeted therapy that will work best for them.

We envision a future where in nearly every interaction patients have with the healthcare system, their genomic information will be sequenced to reveal not just their inherited DNA, but also what changes in their bodies are encoded into circulating DNA, RNA, methylation and proteomics, Almogy said.

Early Application

The $100 genome is already proving its worth. Researchers from Stanford University utilized the low-cost genomic sequencing to investigate the trajectories in precancerous polyps to early colorectal adenocarcinoma. The paper, not yet peer-reviewed but published on bioRxiv, demonstrated the technologys ability to observe changes in DNA methylation that occur early in the malignant transformation process, providing clues as to what happens at a molecular level when a polyp turns cancerous. This type of research could one day translate into clinical use, where physicians could use genomic sequencing to detect DNA changes in cells that might signal the danger of an impending malignant tumor.

Low-cost genomics helps therapeutic development in a couple of fundamental ways, Almogy said. Firstly, many companies seek to understand the genomic drivers of disease by sequencing populations and looking for associations between variants and disease. This type of work inherently requires large numbers to be useful, and the $100 genome certainly enables larger studies in a wider variety of populations. Second, low-cost genomics enables large-scale experiments to reveal the function of many genes.

Ultima isnt prepared to stop at $100 though. As evidenced by its recent collaboration with Exact Sciences, the goal is to continue driving the price down. The companies entered into a long-term supply agreement in June aimed at lowering the cost of sequencing and improving patient access to genomics-based testing. As part of the alliance, Ultima and Exact will develop one or more of Exacts advanced cancer diagnostic tests that will be developed using Ultimas technology. Earlier in June, Ultima paired with Olink Holding AB to combine the latter's Explore assay with its sequencing system to enable larger-scale projects.

Were currently in early access mode, so were focused on optimizing the platform with our initial customers before making it available for broad commercial launch next year, Almogy explained. "Beyond that, we continue to develop improvements in the architecture, because for us the $100 genome is only the beginning and were committed to continuously [driving] down the cost of genomic information.

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Here's how the $100 Human Genome will Change Medicine - BioSpace

Like most geneticists, Ryan Doan learned in school that the vast majority of the genome is useless so-called junk DNA that doesnt code for proteins. But in 2014, while doing postdoctoral research, Doan started to rethink that belief. He was bothered by the fact that autism genetics research, which has largely focused on the coding genome, hasnt made the progress many had hoped for especially in providing autistic people with genetic information that informs potential treatments.

Were not finding as much as we wouldve thought, says Doan, assistant professor of pediatrics at Boston Childrens Hospital in Massachusetts. The next best place is trying to branch out into the noncoding regions.

Now, Doans autism research is primarily focused on the largely unexplored 99 percent of the genome that lies beyond the protein-coding exome. According to his unpublished work, at least 3 percent of autistic people have noncoding mutations that contribute to their condition.

The future seems bright, but the noncoding space will be difficult for quite a while. Ivan Iossifov

Aided by new databases and cheaper whole-genome sequencing, many autism genetics researchers are, like Doan, taking tentative steps into the wide-open noncoding space. Their results so far are mixed, and the challenges remain large. Whole-genome sequencing still costs two or three times as much as exome sequencing, which limits sample size, and the effects of noncoding mutations are likely to be more subtle than those of their coding counterparts, Doan and other scientists say. But many say they hope that probing noncoding DNA will unearth genetic causes of autism for more people and reveal new details about the conditions biology.

The future seems bright, but the noncoding space will be difficult for quite a while, says Ivan Iossifov, associate professor of genetics at Cold Spring Harbor Laboratory in New York. For now, everyone is simply taking baby steps, he says very expensive baby steps.

Researchers had no way to navigate the noncoding genomes 3 billion base pairs until the launch of the Encyclopedia of DNA Elements (ENCODE) in 2003. A little more than a decade later, its spinoff, psychENCODE, started to map gene regulatory elements within that vast uncharted space in the human brain and other tissues work that is still underway.

Those maps made it possible for researchers to begin devising targeted strategies to explore the links, if any, between noncoding mutations and autism. It might be tempting to search the entire noncoding space to ensure that important mutations connected to autism arent missed especially given how little is known about the DNA there. But starting with stretches of DNA with known functions, such as the promoters and enhancers that help regulate a genes expression, stands to increase the likelihood that any discovered mutations will be meaningful.

Some people are very agnostic to location, says Santhosh Girirajan, associate professor of genomics at Pennsylvania State University. And some are looking at some star in some galaxy somewhere.

Promoters the focus of Doans study are located next to the genes they regulate. Enhancers, which may be farther away, carry more mutations in autistic people than in their non-autistic siblings, according to a 2021 analysis. In autistic people, genes linked to autism also tend to have an overabundance of transposons sections of noncoding DNA that can jump randomly around the genome and disrupt other genes another study found.

Iossifov is surveying yet another source of noncoding DNA: stretches located within genes called introns. About 6 percent of autistic people have an intron mutation that likely contributes to their condition, according to his 2021 analysis of these sections in nearly 2,000 autistic children and their non-autistic siblings. To bolster the finding, his team is studying gene expression levels, reasoning that if a gene with an intron mutation has atypical expression in autistic people, its likely that mutation is involved in the condition.

Early results look promising, Iossifov says. Expression abnormalities in a gene are rare enough that they can be used as this very useful filter for pointing at de novo noncoding mutations which might be contributory.

For researchers who are exploring the entire noncoding space, machine learning is proving to be a useful tool. A 2018 analysis of whole genomes from nearly 2,000 families with one autistic and one non-autistic child, for example, initially turned up no relevant noncoding mutations compared with controls. But using a machine-learning tool that identifies multiple types of noncoding variants revealed an excess of mutations in promoter regions among the autistic participants.

Similarly, only a neural network trained on functional genomics data could spot differences between autistic children and their non-autistic siblings across some 200,000 noncoding variants in another 2021 study. More noncoding mutations occurred near autism-linked genes in children with autism than in those without. Overall, though, noncoding mutations occurred equally closely to the nearest gene in both autistic and non-autistic people, highlighting the challenge of identifying these causal mutations, the investigators wrote.

Noncoding and coding mutations may contribute to autism in similar proportions: they are found in about 4.3 and 5.4 percent of autistic children, respectively, according to a 2019 analysis that used machine learning to estimate an individual mutations likelihood of contributing to the condition.

Yet a third strategy involves looking at the whole noncoding space but limiting the analysis to a cohort thats more likely to have rare mutations. A February study of 22 families with high rates of inter-family marriage, for example, found likely disease-causing variants in promoters and enhancers for five autism-linked genes. The team is now using CRISPR to study the variants functions in cells, as well as repeating the work in a new cohort of African children with autism.

Eventually, all of this information in aggregate will be able to tell us about the molecular mechanisms underlying autism, says lead investigator Maria Chahrour, assistant professor of genetics and neuroscience at the University of Texas Southwestern Medical Center in Dallas.

Even when a noncoding mutation contributes to autism, its individual effect is small, the results so far suggest. That means noncoding mutations probably arent acting on their own to cause autism, Girirajan says. Rather, several may act together or in tandem with a coding mutation.

How noncoding mutations affect the genome might also be far more subtle and difficult to nail down than for coding mutations. A given mutation may matter in only one cell type or at a specific point in development, for example. Parsing this kind of complexity, while enormously challenging, could help to explain autisms heterogeneity, Girirajan says. Autism subtypes might reflect not just mutations in a specific gene, but how a genes expression varies across time.

Eventually, all of this information in aggregate will be able to tell us about the molecular mechanisms underlying autism. Maria Chahrour

Its so complex. We are living in a naive land where everything is genes, Girirajan says. What we are not thinking about is gene regulation at different stages of development and tissues. Oh gosh.

To move forward, Girirajan and others say, the field needs to build up whole-genome databases in a big way: At present, autism researchers have access to the exomes of around 50,000 autistic people, and even that has been barely enough to find results in the much simpler coding space, Doan says.

For the noncoding space, you cut your samples 5-fold, but increase complexity 50-fold, he says. You have a huge power problem and thats just something we have to deal with for a while.

Geneticists also need to refine the maps that autism researchers are using to find their way. The ENCODE project, for one, is working to release data on the time periods and cell types in which promoters, enhancers and other regulatory elements influence genes.

Still, results from other fields are encouraging: Other neuropsychiatric conditions are now linked to many mutations in the noncoding region. Of 22 regions implicated in schizophrenia in one large study, for example, 13 are located in noncoding regions within or between genes.

In autism, this is still behind, Iossifov says, but adds that it is only a matter of time before similar findings emerge. Theres no doubt.

Cite this article: https://doi.org/10.53053/WHLV1876

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The final frontier: Autism geneticists take on the noncoding genome | Spectrum - Spectrum

With its multiple variants such as Delta and Omicron, the COVID-19 pandemic highlighted the need for genomic surveillance, to monitor virus evolution and its implications on transmission dynamics and response measures like vaccines. Sequencing informationprovides crucial decision-making information during epidemics and pandemics. On 8-9 June 2022, WHOs Eastern Mediterranean Regional Office convened a meeting in Egypt with partner organizations and countries to discuss the framework for integratedrespiratory pathogen surveillance including the role of genomic surveillance.The regional laboratory focal point set the scene:

Currently, 19 out of the 22 countries in the Eastern Mediterranean Region have genomic sequencing capabilities. A regional network has been established to enable all countries to have access to sequencing, and to strengthen their capacities coherentlyand collaboratively to be able to detect, investigate and respond to COVID-19 and other emerging and re-emerging infectious diseases with epidemic and pandemic potential.

- Dr Amal Barakat, Regional Laboratory Focal Point, WHO

Some highlights from stories shared by countries in the meeting:

Following the significant increase in molecular diagnostic capacity for SARS-CoV-2 in the country enabling up to 250,000 tests per day, the National Influenza Centre at the Ministry of Health (MOH) recognized early that the need for SARS-CoV-2 sequencingwas also increasing. To address this, Morocco set up a national consortium of four laboratories two public and two private to cover different geographic regions in the country.

The Consortium enables us to address genomic surveillance needs by bringing in the capacities and capabilities of the private sector. This was a major achievement and presents an opportunity for us as we think about the next generation of publichealth surveillance.

- Professor Hisham Ouzmil, National Influenza Centre, Morocco

The MOH Central Public Health Laboratory (CPHL) serves as WHOs regional reference laboratory for COVID-19. The CPHL linked with national and local academic partners to strengthen workforce capacities, increase national genomic surveillance coverage,and develop algorithms for selecting cases for sequencing so that virological trends associated with different sub-populations such as travelers, severely ill patients and cases from different geographic regions could be well understood.

Genomics have helped us to better understand the epidemiology of COVID-19 in Oman. Linking genomic data to epidemiological and clinical data, and analyzing trends from other countries maximizes the utility and power of genomics. We are happy towork with other countries, share our experiences and strengthen collaborations as we learn lessons for future pandemic preparedness.

- Dr Hanan Alkindi, Central Public Health Laboratory, Oman

A massive effort was undertaken to expand genomic surveillance so that the viral phylo-dynamics could be understood in all geographic regions of the country and to look at patterns among severe cases, travel-related cases, post-vaccination cases and re-infections.

More than 60,000 SARS-CoV-2 samples have been sequenced from around the country. We have the opportunity to use the capacity established for various public health threats and are ready for future emergencies.

- Dr Ahmed Albarraq, Public Health Authority, Saudi Arabia

Outputs from the meeting and reflections from countries on the role of genomics during the COVID-19 pandemic and future emergencies will enable the Region to plan effectively and focus attention on the future of integrated respiratory pathogen surveillanceinclusive of genomic surveillance. The regional operational framework for integrated surveillance is being finalized and will be available later this year including the opportunities for genomic surveillance in the context of the recently launchedGlobal Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 20222032.

The 10-year Global strategy will enable countries in the Eastern Mediterranean Region, as well as other regions, to capitalize on the gains made and to solidify the role of genomics in future public health practice. Click here to learn more about the Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 20222032.

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The Eastern Mediterranean Region reflects on genomic sequencing and its future within integrated surveillance of respiratory viruses - World Health...

Contributed Commentary by Per Hammer, Cytiva

July 15, 2022 | Historically, the heavily regulated biopharma industry has been slow to adopt new technologies. However, a shift toward automation is vital to ensure that next-generation solutionssuch as cell and gene therapiesare produced at scale.

Less than one in five senior pharma executives strongly believe that frontier technologies, such as artificial intelligence, are widely adopted to support automation and increase the speed of specific processes. With cell therapies approved by global regulatory bodies, it is time to accelerate smart technologies and cell and gene therapy manufacturing.

Todays cell therapy treatments are often made on a small-scale, include manual preparation steps, and are produced for a clinical trial setting. Researchers spend days processing cellular material, monitoring its growth during the expansion phase, and preparing for re-administration to the patient. This process is demonstrated in administering autologous treatments so that every patient receives a unique living drug.

Though the current process is complex, it offers inspiring outcomes. For example, on April 1, 2022, the Food and Drug Administration (FDA) approved Kite Pharmas Yescarta, a chimeric antigen receptor (CAR) T-cell therapy for adult patients with large B-cell lymphoma. This kind of cancer is usually resistant to initial treatment and relapses within one year. With FDA approval, Yescarta (axicabtagene ciloleucel) is now the second-line treatment, a first for an autologous CAR T-cell therapy.

Cell Therapy Enters Mainstream

The exceptional results emerging from cell therapy clinical trials suggest we are entering a new phase of medical treatmentone where we can expect far more from our healthcare interventions than we ever imagined. Following the regulatory approval of autologous CAR T-cell therapies, the global cancer treatment landscape is changing, and the future is bright.

The success of COVID-19 vaccines signaled the arrival of the genomic medicines ageone where we hope to see cell and gene therapies deliver long-term remission and even cures for patients with some of the most complex diseases. According to the Alliance for Regenerative Medicine 2021 Annual Report, nearly 60% of the ongoing regenerative medicine clinical trials studied prevalent diseases by the end of the calendar year. But to get these powerful treatments to those who need them, we must have an automated manufacturing infrastructure that can generate cell therapies to meet increased demand in the coming years.

Saving Time Through Automation

Time is of the utmost importance, as biopharma manufacturing involves patient cells that have limited viability. Manual approaches to cell therapy production are time-consuming, and tasks such as checking cells at regular intervals during expansion are laborious. Another time-draining factor is the workflow and cleaning routines involved in maintaining a safe lab environment.

Automated solutions reduce or remove many of these challenges. After setting up a process, an operator can focus on other things while critical parameters such as temperature, pH level, gas transfer, and flow rates are monitored and controlled without human intervention.

Reducing Risks for Better Results

Manual cell processing solutions are complex, with many checkpoints across isolation, expansion, harvesting, and preservation stages. Unfortunately, each of these steps increases the risk potential. Despite the research teams expertise, there is still a chance that materials could be inadvertently contaminated during numerous open stages.

Additionally, limited process control can lead to difficulties in achieving high reproducibility. An automated modular solution minimizes these risks by bringing multiple steps within a closed, highly regulated, and controlled system.

Improving Manufacturing Efficiency

Changing a manufacturing process requires multiple manual routines and adjustments that must be checked and documented. However, documentation and protocols are less helpful when a customized process is used because they only apply to that specific setup.

Standardization would effectively improve manufacturing efficiency. This approach would ensure that what is learned in one project can be referenced in future work, with data and documentation applicable across different technology applications. A modular chain of connected systems allows for process variation with instruments running in customized configurations. Additionally, having control of an individual instrument leads to the straightforward use of built-in software and sensors.

Automated Manufacturing: The Way Forward for Cell and Gene Therapy

By using automated manufacturing to minimize human interaction, time, and resource requirements, it is possible to increase production speed and lower some risks and costs associated with commercialization.

The industry is ever-changing and adjusting its complex, yet exciting challenges will take some time. However, automation can create a significant advantage over competitors, providing the tools needed to produce cell therapies with the highest levels of safety and efficacy for patients.

Per Hammer has two decades of experience in the biopharma industry, mainly supporting customers in academics through process development and manufacturing. Per joined Cytiva in 2001, taking on several distinct roles in the company. Most recently, he progressed from Product Manager Leader for the Bioprocess Automation and Digital Team to Senior Global Product Manager for the Cell & Gene Therapy Automation and Digital Solutions. He can be reached at per.hammer@cytiva.com.

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Automating the Genomic Medicines of the Future - Bio-IT World

A genetic setup to investigate boundary function in vivo

We previously demonstrated that a 150-kilobase (kb) region, the EP boundary, is sufficient to segregate the regulatory activities of the Epha4 and Pax3 TADs10 (Extended Data Figs. 1 and 2). The DelB background carries a large deletion that removes this boundary region, and the Epha4 gene, resulting in the ectopic interaction between the Epha4 limb enhancers and the Pax3 gene. This causes Pax3 misexpression and the shortening of fingers (brachydactyly) in mice and in human patients. In contrast, the DelBs background carries a similar deletion but not affecting the EP boundary, which maintains the Epha4 and Pax3 TADs and confines the Epha4 enhancers within their own regulatory domain (Fig. 1a and Extended Data Fig. 1).

a, cHi-C maps from E11.5 distal limbs from DelBs mutants at 10-kb resolution. Data were mapped on a custom genome containing the DelBs deletion (n=1 with an internal control comparing 6 different experiments; Methods). The red rectangle marks the EP boundary region. Insets represent a magnification (5-kb resolution) of the centromeric (left) and telomeric (right) loops highlighted by brackets on the map. Cen, Centromeric; Tel, Telomeric. Arrowheads represent reverse- (light blue) and forward- (orange) oriented CBSs. Below, Lac-Z staining (left) and WISH (right) of E11.5 mouse forelimbs show activation pattern of Epha4 enhancers and Pax3 expression, respectively. b, CTCF ChIPseq track from E11.5 mouse distal limbs. Schematic shows CBS orientation. c, Insulation score values. The gray dot represents the local minima of the insulation score at the EP boundary. BS, boundary score. d, Relationship between BS and the number of CBSs (data from ref. 26). The boxes in the boxplots indicate the median and the first and third quartiles (Q1 and Q3). Whiskers extend to the last observation within 1.5 times the interquartile range below and above Q1 and Q3, respectively. The rest of the observations, including maxima and minima, are shown as outliers. N=8,127 insulation minima found in mESC Hi-C matrices. e, WISH shows Pax3 expression in E11.5 forelimbs from CBS mutants. Note Pax3 misexpression on the distal anterior region in R1, F1 and F2 mutants (white arrowheads). Scale bar, 250m. f, Pax3 qPCR analysis in E11.5 limb buds from CBS mutants. Bars represent the mean and white dots represent individual replicates. Values were normalized against DelBs mutant (Ct) (two-sided t-test *P0.05; NS, nonsignificant; P values from left to right: DelBs versus R1: 0.02; DelBs versus R2: 0.11; DelBs versus F1: 0.02; DelBs versus R3: 0.23; DelBs versus F2: 0.02; DelBs versus R4: 0.73). Cen, Centromeric; Tel, Telomeric.

To characterize the EP boundary in vivo, we performed CTCF ChIPseq on developing limbs. This analysis revealed the presence of six clustered CBSs at the EP boundary region (Fig. 1a,b and Extended Data Fig. 2), a profile that is conserved across tissues25,26. CTCF motif analyses confirmed the divergent orientation of these sites, a signature of TAD boundaries, with four CBSs in reverse (R) and two in forward orientation (F). Other features associated with boundaries, such as active transcription or housekeeping genes, were not found in the region27 (Extended Data Fig. 3). cHi-C data from DelBs stage E11.5 distal limbs28 revealed chromatin loops connecting the two forward-oriented CBSs (F1 and F2) with the telomeric boundary of the Pax3 TAD, and the centromeric boundary of the Epha4 TAD with the reverse-oriented CBSs R1, R2 and R3 (Fig. 1a,b). However, the close genomic distances between R2 and F1 and between R3 and F2 preclude the unambiguous assignment of loops to specific sites. RAD21 (cohesin subunit) ChIPseq experiments in E11.5 distal limbs revealed that R1, F1 and F2, as well as R2 and R3 to a lesser degree, are bound by cohesin (Extended Data Fig. 3), an essential component for the formation of chromatin loops21,29,30. These results delineate the EP element as a prototypical boundary region with insulating properties likely encoded and controlled by CBSs.

Boundary regions are predominantly composed of CBS clusters31, suggesting that the number of sites might be relevant for their function. We explored this by calculating boundary scores32 on available Hi-C maps26, and categorizing boundaries according to CBS number. We observe that boundary scores increase monotonically with CBS number, reaching a stabilization at ten CBSs (Fig. 1d). According to this distribution, the EP boundary falls within a range where its function might be sensitive to alterations on CBS number. To test this, we employed a mouse homozygous embryonic stem cell (mESC) line for the DelBs background28, which we edited to generate individual homozygous deletions for each of the six CBSs of the EP boundary region (Supplementary Fig. 1). ChIPseq experiments revealed that the disruption of the binding motif was sufficient to abolish CTCF recruitment (Supplementary Fig. 2). Subsequently, we employed tetraploid complementation assays to generate mutant embryos and measure the functional consequences of these deletions in vivo33,34.

Whole-mount in situ hybridization (WISH) on E11.5 mutant embryos revealed that the insulation function of the EP boundary can be sensitive to individual CBS perturbations (Fig. 1e). However, this effect was restricted to CBSs displaying prominent RAD21 binding (R1, F1 and F2) (Extended Data Fig. 3). The altered boundary function was evidenced by Pax3 misexpression on a reduced area of the anterior limb, while the expression domains in other tissues remained unaltered (Supplementary Fig. 3). The disruption of the other CBSs (R2, R3 and R4) did not alter Pax3 expression, demonstrating that the EP boundary can also preserve its function despite a reduction in CBS number.

To quantify Pax3 misexpression, we performed quantitative PCR (qPCR) in E11.5 forelimbs. Similarly, we observed a modest, but significant, upregulation in R1, F1 and F2 mutants (Fig. 1f). Importantly, the functionality of individual CBSs is not strictly correlated with CTCF occupancy as the deletion of R3, displaying the highest levels of CTCF binding among the cluster (Fig. 1b and Extended Data Fig. 3), does not result in measurable transcriptional changes (Fig. 1f). Thus, while CBS number influences insulation, the characteristics of individual sites are major determinants of boundary function.

To explore CBS cooperation, we retargeted our R1 mESC line to generate double knockout mutants with different (R1+F2) or identical CBS orientations (F1 and F2 in F-all) (Fig. 2a). WISH revealed an expanded Pax3 misexpression towards the posterior region of the limb, demonstrating that the EP boundary is compromised in both mutants. Next, we determined the nature of CBS cooperation by qPCR. These experiments revealed that, in both mutants, Pax3 misexpression exceeded the summed expression levels from the corresponding individual deletions (Fig. 2b). These negative epistatic effects indicate that CBSs are partially redundant, compensating for the absence of each other.

a, WISH shows Pax3 expression in E11.5 forelimbs from CBS mutants. Arrowheads represent reverse- (light blue) and forward- (orange) oriented CBSs. Crosses indicate deleted CBSs. Note increased Pax3 misexpression towards the posterior regions of the limb. Scale bar, 250m. b, Pax3 qPCR analysis in E11.5 limb buds from CBS mutants. Bars represent the mean and white dots represent individual replicates. Values were normalized against DelBs mutant (Ct) (**t-test **P0.01; R1+F2 versus F-all: 0.008). c, cHi-C maps from E11.5 mutant distal limbs at 10-kb resolution (top). Data were mapped on a custom genome containing the DelBs deletion (n=1 with an internal control comparing 6 different experiments; Methods). Insets represent a magnification (5-kb resolution) of the centromeric (left) and telomeric (right) loops highlighted by brackets on the map. Gained or lost chromatin loops are represented by full or empty dots, respectively. Subtraction maps (bottom) showing gain (red) or loss (blue) of interactions in mutants compared with DelBs. d, Insulation score values. Lines represent indicated mutants. Dots represent the local minima of the insulation score at the EP boundary for each mutant. e, Virtual 4C profiles for the genomic region displayed in c (viewpoint in Pax3). The light-gray rectangle highlights the Epha4 enhancer region. Note increased interactions between the Pax3 promoter and the Epha4 enhancer in R1+F2 and F-all (purple and orange) compared with DelBs mutants (gray).

To gain insights on the mechanisms of CBS cooperation, we generated cHi-C maps of the EP locus from E11.5 distal limbs (Fig. 2c and Supplementary Fig. 4). Maps from R1+F2 embryos denoted a clear partition between the EphaA4 and Pax3 TADs, analogous to DelBs control mutants (Fig. 2c). However, subtraction maps revealed decreased intra-TAD interactions for the Epha4 and Pax3 TADs, and a concomitant increase in inter-TAD interactions. In addition, we observed the appearance of a loop connecting the outer boundaries of the Epha4 and Pax3 TADs (meta-TAD loop; Extended Data Fig. 4)35. Accordingly, the boundary score of the EP boundary in R1+F2 mutants was decreased, reflecting a weakened structural insulation (Fig. 2d). Virtual Circular Chromosome Conformation Capture (4C) profiles revealed increased chromatin interactions between the Pax3 promoter and the Epha4 limb enhancers (Fig. 2e), consistent with the upregulation of Pax3. In addition, two of the chromatin loops that connect the EP boundary and the telomeric boundary were abolished, due to the deletion of the F2 anchor and the associated loss of RAD21 (Fig. 2c and Extended Data Figs. 4 and 5). Consequently, the adjacent chromatin loop exhibited a compensatory effect, with increased interactions mediated by the F1 anchor, consistent with higher RAD21 occupancy (Extended Data Figs. 4 and 5). At the centromeric site, the deletion of R1 causes the relocation of the loop anchor towards an adjacent region containing a reverse-oriented (R2) and the only remaining forward CBS (F1). While the loop extrusion model would predict a stabilization at a reverse CBS15,16, the short genomic distance between R2 and F1 precludes an unambiguous assignment of the loop anchor. We also observed increased contacts at R3 and R4, suggesting that these sites are functionally redundant.

Then, we examined cHi-C maps from F-all mutants, which display a more pronounced Pax3 misexpression (Fig. 2b). Interaction maps revealed a partial fusion of the Epha4 and Pax3 domains (Fig. 2c), accompanied by a notable decrease of the boundary score (Fig. 2d). Virtual 4C profiles confirmed increased interactions between Pax3 and the Epha4 enhancers in F-all compared with R1+F2 mutants, in agreement with the more pronounced Pax3 upregulation (Fig. 2e). The deletion of all CBSs with forward orientation abolishes the chromatin loops connecting with the telomeric Pax3 boundary (Fig. 2c and Extended Data Fig. 4). Towards the centromeric side, R1 maintains RAD21 binding and its chromatin loop with the centromeric Epha4 boundary (Extended Data Figs. 4 and 5). However, other chromatin loops are still discernible and anchored by the R3 and R4 sites, confirming that these sites perform distinct yet partially overlapping functions. These results demonstrate that CBSs can cooperate but also partially compensate for the absence of each other, conferring functional robustness to boundaries.

Chromatin loops are predominantly anchored by CBS pairs with convergent motif orientation14,36. Intriguingly, we observed that the combined F1 and F2 deletion (F-all) not only disrupts the loops in the expected orientation (telomeric), but also impacts the centromeric one, as observed in the subtraction maps (Fig. 2c). This effect is noticeable at the R2/F1 site, which was associated with a centromeric chromatin loop in the DelBs background (Fig. 1a). This demonstrates that the main loop anchor point was not the R2 but the F1 site (Extended Data Fig. 4), suggesting that this CBS can form loops in a nonconvergent orientation. Such mechanism is described by the loop extrusion model, which predicts that loops could create steric impediments that might prevent additional cohesin complexes from sliding through anchor sites15,16. This effect would stabilize these additional cohesin complexes, resulting in the establishment of simultaneous and paired nonconvergent and convergent loops (Fig. 3a).

a, Schematic of a convergent loop that indirectly generates a nonconvergent loop in the opposite direction. b, Percentage of loop anchors establishing bidirectional loops (n=12,635 loops from mESCs from ref. 26). Anchor categories: convergent-only (only CBSs oriented in the same direction as their anchored loops, n=7,769), nonconvergent (anchor loops in a direction for which they lack a directional CBS, n=960) and no-CTCF (no CBS, n=3,906). c, Loop strengths in pairs of convergent/nonconvergent loops classified into Non-conv.-associated (nonconvergent loop sharing the nonconvergent anchor with a convergent loop in the opposite direction, n=322) and Conv.-associated (convergent loop sharing one anchor with a nonconvergent loop in the opposite direction, n=496). Boxplots defined as in Fig. 1c. Two-sided BenjaminiHochberg-corrected MannWhitney U-test P=6.2106. d, Aggregated loop signal for categories in c. Arrows represent CBS orientation. e, Pax3 WISH in E11.5 forelimbs from CBS mutants. Arrowheads represent reverse- (blue) and forward- (orange) oriented CBSs. Crosses indicate deleted CBSs. Note the positive correlation between expanded Pax3 misexpression and increased number of deleted CBSs. Scale bar, 250m. f, Pax3 qPCR analysis in E11.5 limbs from CBS mutants. Bars represent mean and dots individual replicates. Values were normalized against DelBs mutant (Ct). Note the positive correlation of Pax3 misexpression with the increase in deleted CBSs (Pearson correlation significantly>0; ***P0,001). g, cHi-C maps from E11.5 mutant distal limbs at 10-kb resolution (top). Data were mapped on a custom genome containing the DelBs deletion (n=1 with an internal control comparing 6 different experiments; Methods). Insets represent a magnification (5-kb resolution) of the centromeric (left) and telomeric (right) loops highlighted by brackets on the map. Gained or lost chromatin loops are represented by full or empty dots. Subtraction maps (bottom) showing gain (red) or loss (blue) of interactions in mutants compared with DelBs. h, Insulation score values. Dots represent the local minima of the insulation score at the EP boundary for each mutant. i, Virtual 4C profiles for the region in g (viewpoint in Pax3). The gray rectangle highlights Epha4 enhancers. Note increased interactions between the Pax3 promoter and the Epha4 enhancers in R-all compared with DelBs.

We searched for further biological indications of this mechanism by analyzing ultra-high-resolution Hi-C datasets26. First, we identified loop anchors and classified them according to the orientation of their CBS motif and associated loops. Loop anchors were split into convergent-only (only CBSs oriented in the same direction as their anchored loops), nonconvergent (anchor loops in a direction for which they lack a directional CBS) and no-CTCF (no CBS). While most loop anchors belong to the convergent-only category14,36, 7.6% of them were classified as nonconvergent. Then, we explored whether these nonconvergent loops could be explained by the nonconvergent anchor simultaneously establishing a convergent loop in the opposite direction (Fig. 3a). We calculated the frequency of anchors involved in bidirectional loops for each category and discovered that, while only 5% of convergent-only or no-CTCF anchors participate in bidirectional loops, this percentage increases significantly up to 45% for nonconvergent anchors (Fig. 3b; chi-squared test, P<10225). To gain further insights into the mechanisms that establish convergent/nonconvergent loop pairs, we calculated the strength of each corresponding paired loop22. We observed that the convergent loops linked to a nonconvergent loop are significantly stronger than their nonconvergent counterparts (Fig. 3c,d; MannWhitney U-test, P=6106). Next, we explore if convergent loops paired to nonconvergent loops are particularly strong in comparison with other types of convergent loops. This analysis revealed that the strength of these convergent loops is similar to other unpaired convergent loops across the genome (Extended Data Fig. 6; single-sided convergent category). However, paired convergent/nonconvergent loops appear to be mechanistically different from unpaired loops, as they are more often associated with TAD corners (Extended Data Fig. 6c; chi-squared test, P<3.5106) and therefore connect anchor points that are located farther away in the linear genome (Extended Data Fig. 6d; MannWhitney U-test, P<4.8108). A comparison against pairs of convergent/convergent loops, which are similarly associated with TAD corners (Extended Data Fig. 6b; category double-sided convergent), revealed that the convergent loops in convergent/nonconvergent pairs are on average stronger (MannWhitney U-test, P=7105). This type of convergent/nonconvergent loops can be observed at relevant developmental loci, such as the Osr1, Ebf1 and Has2 loci (Extended Data Fig. 7). Overall, our analyses suggest that a considerable number of nonconvergent loops could be mechanistically explained by the presence of a stronger and convergent chromatin loop in the opposite orientation and anchored by the same CBS.

To validate these findings in vivo, we sequentially retargeted our R1 mESCs to create a mutant that only retains the forward F1 and F2 sites, which have strong functionality (Fig. 2a,b). During the process, we obtained intermediate mutants with double (R1+R3) and triple CBS deletion (R1+R3+R4), as well as the intended quadruple knockout lacking all reverse CBSs (R-all). WISH revealed an expanded Pax3 expression pattern towards the posterior limb region, an effect that increases with the number of deleted CBSs (Fig. 3e). Expression analyses by qPCR confirmed a significant increasing trend in Pax3 misexpression levels across mutants (Fig. 3f; Pearson correlation>0, P2107). These results demonstrate again that R2, R3 and R4 are functionally redundant sites, despite the absence of measurable effects upon individual deletions (Fig. 1b). However, we noted that Pax3 levels were only moderately increased (threefold) compared with the expression in mutants retaining only-reverse CBSs (ninefold, F-all). Importantly, R-all mutants retain two intact CBSs in the forward orientation, while up to four CBSs are still present in F-all mutants, suggesting that these two forward CBSs (F1 and F2) grant most of the insulator activity of the EP boundary. These experiments indicate that the functional characteristics of specific CBSs can outweigh other predictive parameters of boundary function such as the total number of sites.

As expected, cHi-C maps from R-all mutant limbs revealed a clear partition between the Epha4 and Pax3 TADs (Fig. 3g), consistent with the reduced Pax3 misexpression. Boundary scores at the EP boundary were also only moderately reduced (Fig. 3h), in comparison with the broader effects of the F-all mutant (Fig. 2d). Accordingly, intra-TAD interactions modestly decreased while inter-TAD interactions increased, as also observed in virtual 4C profiles (Fig. 3i). Despite the multiple deletions, the telomeric chromatin loops remained unaffected and anchored by the F1 and F2 sites, both occupied by RAD21 (Fig. 3g and Extended Data Figs. 4 and 5). However, we noticed the persistence of centromeric chromatin loops anchored by the F1 and F2 sites, despite their nonconvergent forward orientation. A higher contact intensity is observed at F1, which would be the first CBS encountered by cohesin complexes sliding from the centromeric side (Extended Data Figs. 4 and 5).

Finally, we investigated if the formation of nonconvergent loops might be associated with the accumulation of cohesin complexes over a limited number of CBSs. We generated a mutant that only retains the R3 CBS (R3-only), which is prominently bound by CTCF (Fig. 1b). We hypothesized that, in the absence of others, this CBS may accumulate the cohesin and form a nonconvergent loop. However, although R3 was the only site able to stall cohesin in this background (Extended Data Fig. 4), cHi-C maps revealed a single convergent loop towards the centromeric side (Extended Data Fig. 8). This loop displays a weak insulator function, denoted by a decreased boundary score, an Epha4 and Pax3 TAD fusion and prominent Pax3 misexpression. Therefore, our results in transgenic mice support our findings at the genome-wide level (Fig. 3ac), demonstrating that specific CBSs can create chromatin loops independently of their motif orientation, seemingly through loop interference.

Previous studies identified divergent CBS clusters as a signature of TAD boundaries, suggesting a role on insulation13,31. While our analysis on mutants with reverse-only CBS orientation (F-all) showed a severe impairment of boundary function (Fig. 2c), this was not the case for R-all mutants, which retain CBSs only in the forward orientation (Fig. 3f). Indeed, the levels of Pax3 misexpression evidenced that insulation is more preserved in R-all than in R1+F2 mutants, which still conserve a divergent CBS signature (Fig. 2c).

This prompted us to explore the relation between CBS composition at boundaries and insulation strength. We examined available Hi-C datasets, classifying boundary regions according to different parameters of CBS composition (that is, number and orientation) and calculating boundary scores (Fig. 4a). Our analysis revealed that, for the same CBS number, boundaries with divergent signatures generally display more insulation than their nondivergent counterparts. However, up to 6% of nondivergent boundaries display scores above 1.0, a value associated with robust functional insulation (Fig. 1c). Manual inspection at specific loci showed that nondivergent boundaries with strong boundary scores present clear TAD partition and no evidence of coregulation for genes located at either side (Extended Data Fig. 9). These results suggest that a divergent signature is not strictly required to form strong functional boundaries.

a, Relation between BSs and the number of CBSs for divergent and nondivergent boundaries in mESC Hi-C data26. Boxplots defined as in Fig. 1c. b, WISH shows Pax3 expression in E11.5 forelimbs from CBS mutants. Arrowheads represent reverse- (light blue) and forward- (orange) oriented CBSs. Crosses indicate deleted CBSs. Light-gray rectangle marks inverted region. Note similar Pax3 misexpression pattern between F-all-Inv and F-all mutants. Scale bar, 500m. c, Pax3 qPCR analysis in E11.5 limb buds from CBS mutants. Bars represent the mean and white dots represent individual replicates. Values were normalized against DelBs mutant (Ct) (two-sided t-test P value). d, cHi-C maps from E11.5 mutant distal limbs at 10-kb resolution (top). Data mapped on custom genome containing the DelBs deletion and the inverted EP boundary (n=1 with an internal control comparing 6 different experiments; Methods). Insets represent a magnification (5-kb resolution) of the centromeric (left) and telomeric (right) loops highlighted by brackets on the map. Gained or lost chromatin loops are represented by full or empty dots, respectively. Subtraction maps (bottom) showing gain (red) or loss (blue) of interactions in mutants compared with DelBs. e, Insulation score values. Lines represent mutants. Dots represent the local minima of the insulation score at the EP boundary for each mutant. f, Virtual 4C profiles for the genomic region displayed in d (viewpoint in Pax3). Light-gray rectangle highlights Epha4 enhancer region. Note similar interaction profile between F-all-Inv (yellow) and F-all mutants (orange).

Next, we explored if the genomic contexts might explain the prominent insulation differences between only-reverse (F-all) or only-forward (R-all) mutants. To evaluate this, we generated a mutant with a homozygous inversion of the boundary region, on the F-all background (F-all-Inv) (Fig. 4b and Supplementary Fig. 5).

WISH and qPCR experiments showed that Pax3 expression is almost indistinguishable from the F-all mutants, both spatially and at the quantitative level (Fig. 4b,c). Moreover, cHi-C maps from F-all-Inv mutants revealed a similar fusion of the Epha4 and Pax3 TADs (Fig. 4d). However, subtraction maps showed a redirection of chromatin loops, which now interact mainly with the telomeric Pax3 boundary instead of the centromeric Epha4 boundary. These ectopic loops are mainly anchored by the R1 site, which preserves its marked functionality. Despite these local differences, boundary scores and virtual 4C profiles remained comparable between F-all-Inv and F-all mutants (Fig. 4e,f). These results suggest that the orientation of entire boundary regions, as well as the differences in the surrounding genomic context, play a minor role in insulator function.

To determine to what extent CTCF binding contributes to the EP boundary function, we generated a sextuple knockout with all CBSs deleted (ALL). WISH revealed a further expansion of Pax3 misexpression, covering the distal limb entirely. This expanded expression mirrors that of DelB mutants, in which the entire boundary region is deleted (Fig. 5a). Expression analyses revealed that Pax3 misexpression in ALL mutants exceeds the combined sum of expression from R-all and F-all mutants (Fig. 5b), again indicating the cooperative and redundant CBS action. Intriguingly, Pax3 misexpression in the R3-only background was comparable to ALL, suggesting that a functionally weak CBS is not sufficient to hinder enhancerpromoter communication (Extended Data Fig. 8). Nevertheless, ALL mutants only reach 65% of the Pax3 misexpression observed in DelB mutants (Fig. 5b), which may be attributed to the 150-kb inter-CBS region that differentiates both mutants.

a, WISH shows Pax3 expression in E11.5 forelimbs from CBS mutants. Arrowheads represent reverse- (light blue) and forward- (orange) oriented CBSs. Crosses indicate deleted CBSs and the gray rectangle represents the deleted region. Note the similarities in expression pattern between mutants. Scale bar, 250m. b, Pax3 qPCR analysis in E11.5 limb buds from CBS mutants. Bars represent the mean and white dots represent individual replicates. Values were normalized against DelBs mutants (Ct) (*two-sided t-test P0.05, ALL versus DelB: 0.03). c, cHi-C maps from E11.5 mutant distal limbs at 10-kb resolution (top). Data mapped on custom genome containing the DelBs deletion (n=1 with an internal control comparing 6 different experiments; Methods). Insets represent a magnification (5-kb resolution) of the centromeric (left) and telomeric (right) loops highlighted by brackets on the map. Gained or lost chromatin loops represented by full or empty dots, respectively. Subtraction maps (bottom) showing gain (red) or loss (blue) of interactions in mutants compared with DelBs (left) and DelB (right). d, Insulation score values. Lines represent mutants. Dots represent the local minima of the insulation score at the EP boundary for each mutant. e, Virtual 4C profiles for the genomic region displayed in c (viewpoint in Pax3). Light-gray rectangle highlights Epha4 enhancer region.

To investigate the reduced Pax3 misexpression in ALL, compared with DelB mutants, we performed cHi-C experiments (Fig. 5c). These experiments revealed a prominent Epha4 and Pax3 TAD fusion, with increased intensity of the meta-TAD loop (Extended Data Fig. 4). This results from the severe disruption of the EP boundary, denoted by a reduced boundary score (Fig. 5d) and the complete absence of RAD21 binding or anchored loops (Extended Data Figs. 4 and 5). In fact, the interaction profile at the EP boundary is not different from other internal locations of the Epha4 TAD (Fig. 5c). Of note, higher insulation is observed in R3-only compared with ALL, despite the comparable Pax3 misexpression between both genetic backgrounds (Extended Data Fig. 8). However, virtual 4C profiles from ALL and R3-only mutants confirmed a similar interaction between Epha4 enhancers and Pax3 (Fig. 5e and Extended Data Fig. 8). These enhancergene interactions were reduced in comparison with DelB, in which Pax3 misexpression is more prominent (Fig. 5e and Extended Data Fig. 8). ChIPseq datasets for epigenetic marks did not reveal additional regions with regulatory potential within the 150-kb region (Extended Data Fig. 3), indicating that the enhanced Pax3 misexpression in DelB mutants is unlikely caused by the deletion of regulatory elements. Taken together, these results suggest that enhancerpromoter distances might influence gene expression levels.

PAX3 misexpression during limb development can cause shortening of thumb and index finger (brachydactyly), in human patients and mouse models10. Therefore, our mutant collection provides an opportunity to study how boundary insulation strengths translate into developmental phenotypes.

We obtained mutant E17.5 fetuses and performed skeletal stainings, measuring relative digit length as a proxy for the phenotype (Fig. 6a,b). First, we analyzed R1 mutants, which displayed moderate Pax3 misexpression in the anterior distal limb (Fig. 1f). Finger length ratios revealed that R1 limbs develop normally, demonstrating that the detrimental effects of Pax3 misexpression can be partially buffered.

a, Skeletal staining of forelimbs from E17.5 mutant and control fetuses. White arrowheads indicate reduced index finger lengths. Black bracket shows the region of the finger measured for the quantification. Finger length correlates negatively with increased Pax3 misexpression. Scale bar, 500m. b, Index lengths relative to ring finger lengths in E17.5 mouse forelimbs. Bars represent the mean and white dots represent individual replicates. Values were normalized on control (CTRL) animals (two-sided t-test **P0.01; two-sided t-test ***P0.001; R1+F2 versus CTRL: 0.007; F-all versus CTRL: 0.0002). c, Correlation between the number of remaining CBSs at the EP boundary and the levels of Pax3 expression in the different mutants described in this study. Pearson regression lines are shown together with R2 values, both for the whole collection of mutants (black) and discarding combined CBS deletions involving CBSs with forward orientation (turquoise). d, Correlation and R2 between BSs and the brachydactyly phenotype penetrance measured as the index to ring finger length ratio for controls, R1+F2 and F-all mutants. The color of the dots represents the level of Pax3 limb misexpression as measured by qPCR. e, Model for boundary insulation as a quantitative modulator of gene expression and developmental phenotypes. Left, a strong boundary (B) efficiently insulates gene A from the enhancers located in the adjacent TAD (E). The boundary shows a cluster of CBSs with different orientations represented with arrowheads. The colored arrow represents a CBS with prominent contribution to boundary function. Middle, the absence of specific CBSs results in a weakened boundary, moderate gene misexpression (limb, indicated in yellow) and mild phenotypes (reduced digits, indicated in red and pointed out by white arrowhead). Right, the absence of the boundary causes a fusion of TADs, strong gene misexpression and strong phenotypes.

In contrast, R1+F2 mutants displayed a moderate reduction of index digit length (Fig. 6a,b), consistent with their increased Pax3 misexpression (Fig. 2b). This demonstrates that weakened boundaries can be permissive to functional interactions between TADs, resulting in altered transcriptional patterns and phenotypes. Importantly, the phenotypes of R1+F2 mutants occur despite an observable partition between Epha4 and Pax3 TADs and across a boundary region displaying high boundary scores (Fig. 2c,d; boundary score=0.8). Analyses on ultra-high-resolution Hi-C datasets26 revealed that many boundary scores fall within the ranges described in our mutant collection (Extended Data Fig. 10). Of note, 40% of boundaries display scores lower than 0.8. According to our observations, those boundaries could be permeable for functional interactions across domains.

Finally, we analyzed the F-all mutants, in which the Epha4 and Pax3 TADs appear largely fused (Fig. 2c). This disruption of TAD organization led to a prominent reduction of digit length (Fig. 6a,b), consistent with the higher Pax3 misexpression (Fig. 2b). Overall, these results illustrate how boundary insulation strength can modulate gene expression and developmental phenotypes, by allowing permissive functional interactions between TADs.

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In vivo dissection of a clustered-CTCF domain boundary reveals developmental principles of regulatory insulation - Nature.com