Burkholderia pseudomallei (Bp) is the causative agent of melioidosis, a disease endemic to the tropics. Melioidosis manifests in various ways ranging from acute skin lesions to pneumonia and, in rare cases, infection of the central nervous system. Bp is a facultative intracellular pathogen and it can infect various cell types. The Bp intracellular lifecycle has been partially elucidated and is highly complex. Herein, we have identified a transcriptional regulator, BP1026B_II1198, that is differentially expressed as Bp transits through host cells. A deletion mutant of BP1026B_II1198 was attenuated in RAW264.7 cell and BALB/c mouse infection. To further characterize the function of this transcriptional regulator, we endeavored to determine the regulon of BP1026B_II1198. RNA-seq analysis showed the global picture of genes regulated while ChIP-seq analysis identified two specific BP1026B_II1198 binding regions on chromosome II. We investigated the transposon mutants of these genes controlled by BP1026B_II1198 and confirmed that these genes contribute to pathogenesis in RAW264.7 murine macrophage cells. Taken together, the data presented here shed light on the regulon of BP1026B_II1198 and its role during intracellular infection and highlights an integral portion of the highly complex regulation network of Bp during host infection. Binding of transcription factors to specific DNA sequences is often controlled through interactions with small ligands, other proteins, and/or by covalent modification3. Control of transcription factors can be mediated through several mechanisms including turnover, sequestration, and synthesis that can also be coordinated by other transcription factors1. Additionally, a transcription factor can bind to several promoter regions, which in turn can be controlled by several transcription factors on a local and/or global scale1. Taken together, this highlights the highly complex system that bacteria use to coordinate a transcriptional response to each environment they confront. Pathogens, in particular, encounter many environments during the course of an infection, requiring them to have a well-coordinated regulation system in place.Bacteria move through various niches responding and reacting to each specific environment they encounter. For this to occur, bacteria must transduce environmental cues through complex signaling pathways that lead to transcription of specific factors required for survival in each situation. These networks are highly complex and require the coordination of many components. Coordinated response to environmental signals often require transcription factors that can modulate RNA polymerase binding to a promoter region, leading to the activation or repression of an operon or geneBurkholderia pseudomallei (Bp) is a facultative intracellular pathogen that causes the disease melioidosis. Bp is endemic in tropical regions with increasing prevalence due to better diagnostics and rising awareness, having a predicted global mortality rate of 54 percent4. Bp encounters many different environments as it infects most tissues of the human body, leading to the formation of localized abscesses, bacteremia, septic shock, and sometimes death6. While infecting a cell, Bp has a dynamic lifecycle that can be broken down into multiple stages: attachment to the host cell, host cell entry, vacuole escape, cytoplasmic replication, and protrusion towards neighboring cells culminating with the spread of infection7. In the vacuole, the Burkholderia secretion apparatus (T3SSBsa), a type III secretion system, must be expressed to allow entry into the host cell cytoplasm8. Within the cytoplasm, Bp can use its secondary flagella or polymerize host cell actin using bacterial expressed BimA to move freely9. Eventually, Bp protrudes towards neighboring cells and uses a virulence-associated type VI secretion system (T6SS-5) to fuse host cell membranes, forming multinucleated giant cells (MNGCs)13. Although we understand the general flow of the Bp intracellular lifecycle, there is much more we do not know about how this pathogen transits through a host and causes the many forms of the disease we cumulatively call melioidosis.Bp requires many virulence factors, some of which are described above. As recently demonstrated, Bp goes through drastic transcriptional changes, with 1,953 genes showing unique patterns of expression during the intracellular infection process7. This finding suggests that Bp has a highly complex regulatory network that is required to coordinate the differential expression of these genes during the progression of infection, from the initial stage of attachment to eventual protrusion towards neighboring cells. The regulation of Bp virulence mechanisms during intracellular infection has been studied to a small extent and mainly focuses on the control of T3SSBsa, T6SS-5, and BimA. The transcriptional regulator BprP regulates expression of the structural components of the T3SSBsa and another transcriptional regulator BsaN15. BsaN with its chaperone, BicA, regulate expression of type III effectors and accessory proteins, the two-component sensor-regulator VirAG that is responsible for downstream expression of T6SS-5, BimA, and BprC, a transcriptional regulator essential for T6SS-5 expression16. Beyond the specific control of characterized virulence pathways, a LysR-type transcriptional regulator GvmR  was identified to control a broad network of genes involved in virulence and metabolism, highlighting the tie between expression of virulence factors and nutrient limitation within the host17. This study advances the understanding of Bp virulence regulation by describing a new transcriptional regulator that is differentially expressed throughout the intracellular lifecycle and required for complete pathogenesis.To cause such a wide array of clinical manifestations and be able to transit through the intracellular environment with such precision, Bp is controlled, we first have to identify transcriptional regulators that control virulence mechanisms during intracellular infection. We identified BP1026B_II1198, a PadR-like transcriptional regulator that is differentially regulated during the Bp intracellular ‘transitome’7. BP1026B_II1198 shows similarities to PadR transcriptional regulators with an N-terminal winged helix-like DNA binding domain (Pfam: PF03551) and a C-terminal virulence activator alpha domain (Pfam: PF10400)19. BP1026B_II1198 is expressed while Bp is initially internalized within endocytic vesicles and later during protrusion as Bp spreads toward neighboring cells   of wildtype Bp pathogenesis, we expressed BP1026B_II1198 in trans and compared the mRNA profile to a control strain with an empty vector control via RNA-seq analysis. Samples were analyzed using Rockhopper21 identifying differential regulation of a subset of Bp genes confirming that BP1026B_II1198 is a transcriptional regulator  ≥ 1 or ≤ -1 were selected for further analysis  in the presence of BP1026B_II1198 while 94 genes were down-regulated (log2FC ≤ -1)  of genes annotated as encoding hypothetical proteins, hypothetical RNA transcripts, or uncharacterized proteins that are controlled by BP1026B_II1198  Fig. . Genes w198 Fig. a. Of the198 Fig. a. While 198 Fig. b using W98 Figs.  and S2.F23 and pyochelin24 are up-regulated by BP1026B_II1198 in cluster 12  that is required for complete pathogenesis and allows intercellular spread and MNGC formation , with a log2FC of 2.09, and a potential Burkholderia specific nitric oxide forming nitrite reductase, BP1026B_II1580 (nirK), with a log2FC of 2.99  by BP1026B_II1198  is a model organism for the study of biofilms and regulation of virulence factor production, which are controlled by a complex network of transcription factors and signaling systems39. Only recently are the subtleties of the Pa transcription factor regulation network coming to light, highlighted by significant functional crosstalk between transcription factors and regulation systems41. Mycobacterium tuberculosis also has a well-documented, highly complex, regulatory network that it uses to navigate hypoxic environments42. To fully understand the pathogenic processes that allow for Bp host colonization and infection we must understand how these pathways are regulated. Some studies have started to shed light on the intricacies of Bp virulence regulation44. In the present study, we add to the knowledge of the Bp regulatory network during intracellular infection through the identification and characterization of BP1026B_II1198.Bacterial transcriptional networks are highly complex and dependent on many variables to properly function. When bacteria are exposed to a unique environment they sense and respond through signaling pathways that lead to the expression of proteins/factors aiding in survival. This is especially true during infection, as pathogenic bacteria encounter various immune defenses within the host. Pediococcus pentosaceus and later characterized to act as repressors of phenolic acid decarboxylase, encoded by the padA gene in Lactobacillus plantarum46. Other PadR-like regulators have been identified in other survival processes including drug resistance, stress responses, biosynthesis of antibiotics, and carbon catabolism53. The Pfam database indicates that there are over 19,000 sequences containing a PadR-like domain and over 2,900 sequences representing the domain architecture of BP1026B_II1198 with the N-terminal PadR domain and a C-terminal virulence activator alpha domain19. PadR domains have also been shown to be in combination with many other domains with a total of 35 domain architectures identified in the Pfam database19. Bp does not contain a padA homolog or a phenolic acid metabolic pathway, and the varied nature of PadR-like proteins suggests broad and diverse functionalities. We therefore took a broad approach to study BP1026B_II1198.BP1026B_II1198 is annotated as a PadR-like transcriptional regulator. PadR regulators were first identified in Bp intracellular lifecycle and is required for in vitro and in vivo pathogenesis, highlighted by the 100% attenuation of the BP1026B_II1198 mutant in the BALB/c mouse model. These two observations show that Bp precisely controls pathogenic function within the host. To further understand this contribution to pathogenesis we identified the genes controlled by BP1026B_II1198 using RNA-seq. We observed that 79.8% of genes differentially regulated in the presence of BP1026B_II1198 have no known functions. This is a striking increase from the 26.7% of genes annotated as hypothetical unknowns within the Bp 1026b genome, suggesting that many pathogenic and survival pathways are yet to be discovered. The presence of BP1026B_II1198 during the later stages of infection coincides with the regional expression around the T6SS-5 locus, aligning with the current understanding of the Bp lifecycle. In addition, expression of BP1026B_II1198 leads to the up-regulation of several secondary metabolite gene clusters malleobactin23, pyochelin24, and malleilactone25, that have been previously identified to be involved in iron acquisition and pathogenesis. The regulation of these known virulence mechanisms likely contribute to the attenuation of the BP1026B_II1198 mutant, although we speculate that there are other factors that are also involved.BP1026B_II1198 is differentially expressed during the 54, their function is diverse and has been implicated in the modulation of gene expression55. Fe-S clusters can respond to increased oxidative stress, iron levels within the host, or other environmental signals during infection leading to an adaptive response56. The up-regulation of genes involved in Fe-S assembly  by BP1026B_II1198, could be induced in response to the intracellular environment, where iron-limitation, reactive-oxygen-species, and other environmental cues requires the sensitive nature of Fe-S regulatory complexes. This result correlates with previous reports showing up-regulation of Fe-S assembly genes in Burkholderia mallei, a clonal derivative of Bp, when exposed to sub-inhibitory levels of nitric oxide57. Genes involved in nitrogen metabolism were also up-regulated, showing that BP1026B_II1198 is partially responsible for a shift in general metabolism during infection. In addition to this, the BP1026B_I1020 mutant showed a reduction during intracellular replication in RAW264.7 cells.We probed many genes that were regulated by BP1026B_II1198 to identify contributors to pathogenesis, including genes that may have a less direct pathogenic function. Although Fe-S clusters were first identified in 1962 to be involved during electron transport58. The function of this protein should be investigated further and could potentially be manipulated as a novel therapeutic target or subunit vaccine component. BP1026B_II1736 sits within the second type III secretion system (T3SS-2) in Bp and encodes SctK, a cytoplasmic structural component of the type III injectasome59. This protein along with SctQ and SctL are referred to as the ‘sorting platform’ and play a critical role on the order in which effector proteins are secreted60. Beyond the implications the specific potential function of BP1026B_II1736, it has been shown that T3SS-2 is involved in the pathogenesis of Solanum lycopersicum (tomato) but not mammalian cells37. While T3SS-2 is not singly required for complete pathogenesis in mammalian models of infection61, our data suggest that this system could contribute to pathogenesis.Other mutants of genes regulated by BP1026B_II1198 that showed reductions in RAW264.7 pathogenesis have less clues as to their function. BP1026B_II1506 is a GntR family transcriptional regulator that could have many downstream gene expression changes that contribute to the reduction in virulence while BP1026B_II1581 is a hypothetical protein that is worth further investigation. BP1026B_I1775 is a Sec-secreted lipoprotein with cleavage site located between amino acids 28 and 29 as identified by SignalP5.0 analysis50. Subsequent mutant analysis of these genes showed a 52% and 83% reduction in wildtype intracellular replication. BP1026B_II0600 encodes a levansucrase precursor, which to our knowledge, has yet to be described as a virulence factor during infection of mammalian cells. However, in plant pathogens like Pseudomonas syringae, levansucrase is critical for the formation of levans that contribute to exopolysaccaride production63. Because Bp is an incidental host of mammals, and freely lives within the environment, the expression of levansucrase could indicate that this gene is used for adaptation to multiple conditions. BP1026B_II1006 is annotated as a DNA binding protein but further analysis using Phyre264 predicts structural similarities to an Fe-S complex containing transcriptional regulator, RrsR65. We predict that the down-regulation of BP1026B_II1006 by BP1026B_II1198 plays a major role in the indirect gene expression changes that occur in our data set. Although outside of the scope of the present work, this data warrants further analysis of the regulation network of BP1026B_II1006.Large numbers of genes/pathways are controlled indirectly through the expression of BP1026B_II1198 as described above. To find the direct DNA binding regions of BP1026B_II1198 we used a ChIP-seq strategy. Through this approach we identified two major regions of DNA binding in intergenic regions of divergently transcribed genes. BP1026B_II1198 showed up-regulation of BP1026B_II0600 and down regulation of BP1026B_II1006. This suggests that BP1026B_II1198 has a dual function as an activator and as a repressor, similar to other PadR-type regulators previously describedBp intracellular transit through the host by activation/repression of multiple factors and pathways. Although, no single virulence factor controlled by IprA shows the same drastic decrease in pathogenesis we believe the cumulative effect of the changes in transcription by IprA highlights its importance during pathogenesis. We believe that in the intracellular environment, many transcription factors, including IprA, work in unison to control how Bp responds to specific environments, allowing for infection to occur. The future study of many of these transcriptional regulators (i.e. BP1026B_II1006 and BP1026B_II1506) will increase the resolution of what we know about the Bp intracellular lifecycle and Bp gene expression within the host. Although the data represent the expression profile of one transcription factor, it is one step towards a better understanding of the intricacies of Bp intracellular pathogenesis.Here we show a snapshot of what is going on during intracellular infection through the lens of the transcriptional regulator BP1026B_II1198 that we would like to tentatively name Intracellular Pathogenesis Regulator A (IprA). The loss of IprA results in significantly reduced pathogenesis in RAW264.7 murine macrophages and in BALB/c mice. This investigation identified that IprA functions as a transcriptional regulator during Escherichia coli strain EPMax10B (BioRad), E1869, and E1354 were used for cloning or plasmid mobilization into Bp as described previously67. Bp 1026b and its select agent regulation excluded analogue, Bp82, were used as described in specific sections below. Luria–Bertani (LB) medium (Difco) or 1 × M9 minimal medium supplemented with 20 mM glucose (MG) were used to culture all strains. MG media was supplemented with 0.3% glyphosate (GS) or 0.1% chlorinated phenylalanine (cPhe) when appropriate. Growth media for Bp82 was supplemented with adenine and thiamine as previously described68. Experiments involving the manipulation of Bp were conducted in a CDC-approved and registered facility at the University of Hawaiʻi at Mānoa or Colorado State University. All work with the select agent Bp was approved by internal review and adhere to recommendations set forth in the BMBL, 5th edition69 for BSL3 organisms.Bp 1026b was used as a source of mutants to test for potential virulence factors36. Mutants of BP1026B_I0528, BP1026B_I0950, and BP1026B_II1464 were not tested because the library did not have insertions in any of these genes36. Transposon mutants available in the transposon library were struck for isolation on media containing kanamycin and each insertion site sequenced to confirm correct transposon insertion location (data not shown). The transposon insertion in BP1026B_II1769 was not validated and confirmed to be present in another gene, BP1026B_II2115, a putative diguanylate cyclase. BP1026B_II1769 lacked a second transposon insertion. All other transposon mutants tested were confirmed to be in the gene of interest by sequencing.The sequence-defined transposon mutant library of 71. An in-frame deletion mutant of BP1026B_II1198 was generated in Bp 1026b using lambda-red recombineering with minor modification72. Lambda red genes were PCR amplified from pKaKa1 and co-incubated with the knockout fragment of BP1026B_II1198 and selection carried out on glyphosate. Glyphosate resistant colonies were purified and patched on MG + 0.3% GS and screened for presence of the gat gene to confirm the deletion BP1026B_II1198. The gat-pheS cassette was removed via Flp recombination and growth on MG + 0.1% cPhe. Phenotypes were confirmed after purification and patching on corresponding media  and 2779 (5’-TCG AAG CTT CAA GCC GAC AAC GAT CTT) digested with NdeI and HindIII and ligated into NdeI/HindIII-cut pAM3GIQ-3xTY1  and goat anti-mouse conjugated to HRP (Invitrogen G-21040)  Fig. c.68. All strains were first grown overnight in LB broth at 37 °C, harvested the following day, diluted to an OD630 of 0.1 in 200 µl of fresh LB, and placed into 96 well plate. Growth curves were carried out at 37 °C shaking and data was recorded with the BioTek ELx808IU plate reader, measurements taken at OD630 every 30 min.Growth analysis was carried out as previously described with minor modifications73 with minor modifications. Briefly, RAW264.7 murine macrophages were seeded for infection at 80–90% confluence on Corning CellBIND culture plates, allowed to attach overnight, washed twice with 1XPBS, and infected the following day. For intracellular replication assays, Bp 1026b, the BP1026B_II1198 mutant, or the BP1026B_II1198 complement (mutant with pAM3GIQ-3xTY1-BP1026B_II1198) were allowed to infect monolayers at an MOI of 1:1, washed with 1XPBS, and then DMEM supplemented with 10% FBS, 0.1 mM IPTG to drive expression of the complement, 700 µg/mL amikacin and 700 µg/mL kanamycin were added to kill any extracellular bacteria. At 2, 8, and 24 hpi, infected monolayers were lysed with 0.2% Triton X-100. Serial dilutions of lysates were plated on LB and colony-forming units (CFU) per well were determined. MNGC formation assays were carried out as intracellular replication assays, with the exception that 1.2% low-melt agarose was added to DMEM after infection allowing the formation of MNGCs. At 24 h post infection, monolayers were fixed with 4% paraformaldehyde, agarose plugs removed, stained with 0.05% crystal violet, and MNGC diameters measured with the Zeiss Axio Observer D1 and the AxioVision 64 bit 4.9.1 software. Cell infection assays with mutants from the Bp 1026b::T24 transposon mutant library were carried out as described above although kanamycin was removed and 1,500 µg/mL amikacin added to remove extracellular bacteria post infection. Monolayers were lysed at 24 h post infection as described above. Percent wildtype replication (%WT) was calculated by the following formula: %WT = (CFUmutant/mean CFUWT) × 100.Intracellular replication and MNGC formation assays were carried out as previously describedBp 1026b and the BP1026B_II1198 mutant as previously described7. Each infection group had 5 mice and the inoculating dose was 4,500 CFU via the intranasal route after mice were anesthetized with ketamine (100 mg/kg) and xylazine (10 mg/kg). Mice were monitored for 62 days post infection and organs  from surviving mice were harvested, homogenized in PBS, serial diluted, and plated on LB. The only surviving mice that survived the length of the study were mice infected with the BP1026B_II1198 mutant. The organ loads show the persistence of this mutant within the BALB/c mouse. No mice were excluded from the study or analysis. Survival curves were plotted using Prism software and analyzed by Log-rank (Mantel-Cox) test (GraphPad).BALB/c mice (4 to 6 weeks old) were purchased from Charles River Laboratory, allowed to acclimatize for a week, and then infected with Bp82 expressing BP1026B_II1198 from pAM3GIQ-3xTY1-BP1026B_II1198 was grown overnight and sub-cultured to mid-log phase in LB + adenine + 0.1 mM IPTG in triplicate. An empty vector (pAM3GIQ-3xTY1) was used as a control. Total RNA was harvested using RNeasy Mini Kit (Qiagen) with on-column (Qiagen) and off-column (Epicentere) DNase digestion steps. RNA samples were sent to Tufts University Genomics Core (TUCF Genomics) for library preparation and Illumina 50 bp single-end reads were sequenced on the Illumina HiSeq 2500. RNA-seq data was analyzed with Rockhopper21 to identify individual genes controlled by BP1026B_II1198 and WoPPER22 to determine larger regions of the genome that are controlled by BP1026B_II1198. RNA-seq data was validated by RT-qPCR using BP1026B_I0774, BP1026B_II1384, and BP1026B_II0521 as housekeeping genes due to consistent expression (log2FC = 0) between conditions  and secondary antibodies conjugated to magnetic beads (Diagenode C03010022). DNA–protein complexes were washed, decrosslinked, treated with RNaseA and proteinase K, and purified with QIAquick PCR purification kit. Immunoprecipitated DNA was sent to TUCF Genomics where DNA libraries were prepped and 50 bp single-end reads were sequenced with the Illumina HiSeq 2500. ChIP-seq data was aligned to the Bp 1026b genome with Bowtie275, peaks called with MACS276, and consensus binding regions determined with MEME38.ChIP-seq was carried out as previously described with minor modificationsAll animal studies described in this manuscript were approved by the Institutional Animal Care and Use Committee at the University of Hawaiʻi at Mānoa (Protocol No. 10-1073), conducted in compliance with the NIH  Guide for the Care and Use of Laboratory Animals, and in compliance with ARRIVE guidelines.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Pausing of transcribing RNA polymerase is regulated and creates opportunities to control gene expression. Research in metazoans has so far mainly focused on RNA polymerase II (Pol II) promoter-proximal pausing leaving the pervasive nature of pausing and its regulatory potential in mammalian cells unclear. Here, we developed a pause detecting algorithm (PDA) for nucleotide-resolution occupancy data and a new native elongating transcript sequencing approach, termed nested NET-seq, that strongly reduces artifactual peaks commonly misinterpreted as pausing sites. Leveraging PDA and nested NET-seq reveal widespread genome-wide Pol II pausing at single-nucleotide resolution in human cells. Notably, the majority of Pol II pauses occur outside of promoter-proximal gene regions primarily along the gene-body of transcribed genes. Sequence analysis combined with machine learning modeling reveals DNA sequence properties underlying widespread transcriptional pausing including a new pause motif. Interestingly, key sequence determinants of RNA polymerase pausing are conserved between human cells and bacteria. These studies indicate pervasive sequence-induced transcriptional pausing in human cells and the knowledge of exact pause locations implies potential functional roles in gene expression. RNA polymerase II (Pol II) is the key enzyme responsible for the transcription of all nuclear protein-coding and a large set of non-coding genes in eukaryotic cells . Pol II trans-acting factors that are involved in the establishment and release of promoter-proximal pausing by Pol II in mammalian cells , relies on deep sequencing of the 3′-ends of the nascent RNA providing DNA strand-specific Pol II densities with single-nucleotide resolution ,28,36. AIn this combined computational and experimental study, we investigated the pervasive nature of transcriptional pausing primarily in human cells and the role of DNA sequences as potential pause determinants. We developed a new algorithm to identify peaks with high precision from nucleotide resolution occupancy profiling data, complemented by the development of a NET-seq approach that minimizes the formation of artifactual signal peaks commonly misinterpreted as pausing locations. Application of this peak caller on NET-seq data provides an atlas of transcriptional pausing sites with single-nucleotide resolution in human cells. Notably, this analysis uncovers pervasive transcriptional pausing of Pol II outside of promoter-proximal gene regions and a new consensus pause motif underlying promoter-proximal pauses. Machine learning modeling reveals the DNA sequence features that are predictive for pausing. Interestingly, key determinants for DNA sequence-induced transcriptional pauses are conserved from bacteria to human cells.HEK293T  and HeLa S3  cells were cultured in DMEM (ThermoFischer Scientific) containing 10% FBS Superior (Biochrom) and 5% penicillin–streptomycin (ThermoFischer Scientific).10CTGTAGGCACCATCAAT/3′-ddC; 5′-rApp: 5′-riboadenylate; 3′-ddC, 3′-dideoxycytidine). Due to this increase in the length of the oligo, the size selection of ligated RNA, cDNA and the PCR product was adapted to 60–130 nt, 100–180 nt and 150–200 nt, respectively. For the nested NET-seq library preparation, the following additional modifications were introduced. For library preparation, 1 μg RNA was used as an input per sample. Reverse transcription was performed as described before with the following primer: 5′-Phos/ATCTCGTATGCCGTCTTCTGCTTG/iSp18/CACTCA/iSp18/TCCGACGATCATTGATGGTGCCTA-3′  that aligned to mispriming prone positions and for which the UMI sequence had at least a 50% similarity with the genomic sequence downstream of the aligned read. The percentage of RT mispriming events that escaped detection was calculated as the ratio of mispriming events for which the UMIs were not identical with the genomic sequence and all mispriming events (including reads with UMIs exactly matching the genomic sequence).i for which i−x1< ix > ix+1, where ix is the signal intensity x at the position i. In the second step, a nonparametric resampling approach is applied for every detected peak to test if this peak has a significantly higher value than the expected value of the maximum given the local Pol II density. Local Pol II density for the position i is described by the number of reads M and the number of positions l with non-zero signal within the window of a given length L (here 200 nucleotides) centered at the position i. A value of local maximum is simulated by redistributing M reads over l positions and extracting the maximum number of reads per position from the newly obtained read distribution. The redistribution is conducted following the null hypothesis that Pol II does not accumulate at any position and a read has an equal probability of being assigned to each of the positions in the local window of length l. Such resampling generates a pool of N (here 10 000) simulated values of local maxima. Next, the P-value is estimated using the fraction of simulation experiments in which the simulated value of local maximum is higher than the observed local maximum. To control for multiple testing, all P-values are corrected using the Benjamini–Hochberg procedure. For further analyses, we considered pausing sites with a corrected P-value <0.05 as significant.The algorithm was designed for detecting signal intensity peaks from single-nucleotide resolution Pol II occupancy data corresponding to potential transcriptional pausing sites. The algorithm consists of two main steps: peak detection and peak evaluation. In the first step, all local maxima are detected. A local maximum is a nucleotide position For calculating the percentage of significant peaks called at the same nucleotide position in NET-seq replicate measurements, only peaks for which a local maxima was detectable in all replicates were included. The percentage of significant peaks was calculated as the ratio of the number of significant peaks common in all replicates and the number of all significant peaks called in a given replicate only. For the two-sided Fisher exact test, all peaks were included regardless whether they were detectable in all replicates.For comparison of pausing sites between different human cell lines, the reported percentage of signal intensity peaks that occur at the same nucleotide position was calculated as the ratio of peaks detected in both cell lines and the number of all peaks detected in the cell line with the lower sequencing depth.Based on their location, pausing sites were classified into one of four major categories: promoter-proximal, gene-body, antisense or intergenic. For defining the regions, we used GENCODE annotations (v28 and v29). A pausing site was classified as ‘promoter-proximal’ if located within 300 nucleotides downstream of the transcription start site (TSS). Pauses between +301 and the 3′-most polyadenylation (pA) site of a gene were classified as ‘gene-body’ pauses. If the pausing site was situated on the opposite strand of a gene in a region between 1000 nucleotides upstream of the most upstream TSS and the most downstream pA site, the pausing site was classified as ‘antisense’. In case of an overlap between the gene-body region of one gene and the antisense region of another gene, the pausing location remains undetermined. All pausing sites located outside the listed regions  were classified as intergenic pauses.The four main categories were further specified into their respective subclasses. Gene-body pausing sites were ‘exonic’ if they overlap with annotated exons, otherwise they were labeled ‘intronic’. Antisense pausing sites were categorized in ‘divergent’ if the pausing site was located upstream of the TSS or ‘convergent’ if it was downstream of the TSS overlapping with the gene-body. In case the pausing locations overlapped, the type of antisense pausing remained undetermined. Intergenic pausing sites occurred at the termination zone, located within 3.5 kb downstream of the pA site or at enhancer regions. The enhancer locations were determined using FANTOM5 enhancer annotation .x, x+20] nucleotides downstream or upstream of a pausing location, where distance x depends on the region of the pausing site . Pausing and non-pausing sites are subsequently referred to as ‘genomic sites’.We defined high-confidence pausing sites as pausing sites that were detected in all biological replicates at the same nucleotide position. Only high-confidence pausing sites were used for downstream analysis and to create a set of pausing sites (positive set). A set of non-pausing sites (negative set) was generated by sampling random positions at which pausing does not occur. To avoid creating artificial differences between both sets, a non-pausing site was sampled from the region [For gene expression quantification, we used RSEM v1.3.1  in the pFor the comparative analysis of signal intensities between promoter-proximal and gene-body pausing, only genes with pauses in both regions were included. Non-expressed genes were not explicitly excluded. However, the lowest TPM that was observed for example genes was >3.Pausing sites were classified as ‘proximal’ when located within 40 nucleotides of a splice site, otherwise they were labeled ‘distant’. The proximal sites are further categorized into: ‘first exon-intron’, ‘intron-exon’, ‘exon-intron’ and ‘last intron-exon’ boundaries. If a pausing site was found in proximity of multiple boundaries, it was assigned to all categories.For every gene with ≥2 pausing sites, we defined the average pausing distance as the mean distance between pausing sites that are located within the most upstream active TSS and the most downstream active pA site.P-value cut-off of 0.05.The GO enrichment analysis was conducted for genes with a short average pausing distance using over-representation analysis of the ConsensusPathDB package . BiologiNET-CAGE data available for HeLa cells were obtained from  using GERNA 3′ end-seq of nascent and newly synthesized (4sU-enriched), polyA(+) and polyA(-) RNA data available for HeLa cells were obtained for exosome depleted cells from  using GEDNA sequences centered on genomic pausing sites were extracted. The corresponding non-pausing set was used to derive the background nucleotide distribution. Extracted pausing sequences and background frequencies were used to create enrichment logos with Logolas .Sequence characteristics that were previously implicated in pausing were included in the model. The determined features for all species included differences in nucleotide skewness and identity at positions of interest, thermodynamic features of the RNA-DNA hybrid, and free energy of the nascent RNA. A comprehensive list of features is given in XY skewness is defined as |X| denotes the number of nucleotides X. X and Y represent standard DNA nucleotides. A difference of XY skewness for all pairs of DNA nucleotides was calculated between the positions located 10 nucleotides upstream and downstream of the genomic site in a 20 nucleotide window.The nucleotide identity was extracted from the +1, -1, -2, -3, -10, -11 position from the genomic site, corresponding to the active center of the RNA polymerase and both ends of the RNA-DNA hybrid.Thermodynamic features, such as the entropy, enthalpy, Gibbs free energy and the melting temperature of the 10 nucleotide RNA-DNA hybrid, were calculated using MELTING  with theThe minimum free energy of the stretch of a nascent RNA between positions -11 and -29, where position -1 corresponds to the last nucleotide added to the nascent RNA, was calculated using the ViennaRNA package . The regAdditional features were calculated for human cell lines since the following tools and databases were only available for human samples. The human-specific features include the DNA shape and form, binding motifs of transcription factors (TFs) and RNA-binding proteins (RBPs), and DNA methylation.DNAshapeR  was usedThe presence of non-B DNA forms in the region 100 nucleotides upstream or downstream of the genomic site was also taken into consideration. Regions of the human genome that tend to form non-B DNA structures such as Z-DNA, A-phased repeats, inverted repeats, mirror repeats and direct repeats were extracted from non-B database v2.0 . AdditioThe presence of a transcription factor binding motif (TFBM) was determined for three regions referred to as ‘polymerase footprint’ (25 nucleotides upstream and downstream of the genomic site), ‘upstream’ (100 nucleotides upstream of the polymerase footprint) and ‘downstream’ (100 nucleotides downstream of the polymerase footprint). About 810 position weight matrices (PWMs) describing 639 human transcription factors were downloaded from the JASPAR database . To limiAbout 1194 PWMs describing human RNA-binding protein (RBP) motifs were downloaded from ATtRACT database . To limiIn addition to purely sequence-dependent features, the DNA methylation levels were added to the model, as DNA methylation has previously been implicated in promoter-proximal pausing . Whole-gn sites, with two equally sized sets of pausing and non-pausing sites. The random forest classification model was implemented with the scikit-learn Python package and uses p predictor variables (genomic features), which showed a variable degree of correlation between each other. About 20% of the n sites were used to optimize the number of decision trees, the maximum number of levels, the minimum number of samples required to split a node and the minimum number of samples required at each leaf node. The model was trained and tested on the remaining 80% of observations using 10-fold cross-validation . The model performance was assessed using the area under the curve (AUC) values of the precision-recall curve. The importance of each feature was computed as permutation importance. Variables with high positive values corresponded to important features for classification, whereas variables with values close to zero or negative corresponded to noise and were not informative. The number of sites n, the number of predictor variables p and the number of folds k together with additional parameters of each model are listed in Machine learning models were developed to distinguish pausing from non-pausing sites based on the genomic features. Each model was tuned, trained and tested on Current peak calling algorithms that have been mainly developed for ChIP-seq data were not applicable to our NET-seq data sets because of the following reasons. First, the width and overall shape of Pol II occupancy peaks obtained from NET-seq and ChIP-seq strongly differed Figure , due to P-value <2.2−308; Fisher exact test) of replicate 1 or 2 were called at the same nucleotide position, respectively  occurred at the same nucleotide position in both replicates  introduced during NET-seq library preparation. Moreover, UMIs also helped to identify reads that resulted from RT mispriming due to misalignment of the RT primer to adapter-like sequences within the nascent RNA Figure ). We comNotably, we found that despite the usage of UMIs a substantial fraction (>30%) of RT mispriming events escaped detection. In order to avoid the formation of peaks from RT mispriming, we developed a new NET-seq protocol variant, termed nested NET-seq Figure . Nested R > 0.8, The nested NET-seq replicates obtained from HeLa cells correlated well indicating the robustness of this approach Figure . The corWe applied PDA to high-coverage nested NET-seq data generated for HeLa cells to obtain a high-resolution genomic map of Pol II transcriptional pausing sites in human cells. We called 4831 significant and high-confidence Pol lI occupancy peaks corresponding to potential Pol II pausing sites. The number of pausing sites was likely an underestimate due to the stringent criteria applied for peak calling and for statistical tests. About 75% and 20.6% of occupancy peaks were intra-  or intergenic (including divergent antisense), respectively Figure . About 3Notably, we found that the majority of Pol II pausing sites occurred outside of promoter-proximal regions, mainly throughout the gene-body Figure . The sigThe average distance between consecutive pausing sites varied strongly between genes Figure . For theGiven the prevalence of Pol II occupancy peaks throughout the gene-body region, we asked if they partially emerged from transcription initiation events at alternative promoters located within genes or from premature RNA cleavage and transcription termination. To address the first question, we re-analyzed NET-CAGE data  that proSince NET-seq provides DNA strand-specific Pol II occupancies , this anPDA also uncovered a large fraction of potential pausing sites in intergenic regions. The majority of these peaks were located at enhancers, the termination zone  and We extended our analysis to other human cell types for which high-coverage NET-seq data were available including MOLT4 , HEK293TTogether, these data suggest widespread Pol II pausing during transcription in human cells and are not restricted to promoter-proximal regions or to transcription in the sense direction.-10Y-2G-1Y+1, where Y is a pyrimidine, differed from known cis-motifs implicated in promoter-proximal pausing in Drosophila and mammalian cells  Figure . This patrans-acting transcription factors in Pol II pausing throughout gene-body regions. To further explore the molecular mechanisms of gene-body pausing regulation by different transcriptions factors represent an interesting subject for future investigations. Together, these results suggest that DNA-sequences underlie pervasive transcriptional pausing in human cells, and also imply potential contributory roles of trans-acting factors.Previously, we found that Pol II can accumulate upstream and downstream of distinct transcription factor binding sites (TFBSs) located throughout the gene-body region . TherefoGiven that DNA sequence properties underlie pervasive pausing, we asked whether we could use this knowledge to predict the pausing potential for each nucleotide along genes. To address this question, we created a machine learning model for transcriptional pausing that was based on a random forest classifier. We first focused our modeling on promoter-proximal Pol II pauses. We tested whether we could predict promoter-proximal pauses from DNA sequence features genome-wide. Notably, our model predicted pausing sites with high accuracy assessed by 10-fold cross-validation Figure . In addiWe found that among the most important predictors for Pol II pausing in the promoter-proximal region were nucleotides at positions +1 (insertion site for incoming NTP) and -1 (last nucleotide of the RNA-DNA hybrid) of the DNA template strand Figure . FurtherWe next performed machine learning on gene-body pauses to investigate whether they were determined by similar or distinct features as compared to pauses in the promoter-proximal region. Our approach could predict gene-body pauses, although at a lower accuracy as compared to promoter-proximal pauses Figure . InteresGiven our finding that the DNA shape was predictive for widespread gene-body pauses and to a less extent also for promoter-proximal pauses, we further characterized the contribution of DNA shape features in Pol II pausing. Interestingly, this analysis revealed that several DNA shape characteristics were linked to widespread pausing in promoter-proximal and gene-body regions Figure . For proEscherichia coli) (Saccharomyces cerevisiae)   , buddingevisiae)  and planhaliana) . We usedhaliana) . Next, whaliana) ,80.E. coli since it was shown that RNA polymerase pauses ubiquitously during transcription in bacteria  had an even stronger effect on pause induction , global run-on sequencing (GRO-seq), ChIP-seq and RNA-seq ,67,68. TThe transcriptional pausing landscape of Pol II in human cells is more diverse than originally anticipated. The majority of Pol II pausing sites that we have detected are located outside of promoter-proximal gene regions. A large fraction of these pauses are distributed along the gene-body. The frequency of pausing sites along the gene-body strongly varies between genes. We found active genes with no or only one high-confident pausing site and genes with pauses every 140 nts. The latter observation suggests that transcription elongation by Pol II can be very discontinuous at a large set of genes where productive elongation is frequently interrupted by pauses. A subset of these gene-body pauses are strategically positioned at splice junctions and especially at the first exon-intron and the last intron-exon boundary. These observations are consistent with the emerging view that pausing is required for co-transcriptional splicing ,69. Pauscis-element could be retrieved for gene-body pauses. Second, the predictability of gene-body pauses is lower as for promoter-proximal pauses. Third, RNA-DNA hybrid features and characteristics of the upstream DNA are more critical determinants for gene-body pauses as compared to promoter-proximal pauses. Moreover, DNA methylation is only linked to a set of gene-body pauses. These differences support the idea that gene-body pauses are distinct and more heterogeneous as promoter-proximal pauses that usually occur within a narrow region downstream of the transcription start site.Are gene-body pauses distinct from pauses in promoter-proximal gene regions? Interestingly, a large set of promoter-proximal and gene-body pauses occur non-randomly at the same genomic nucleotide position in biological replicate measurements. Moreover, distinct DNA sequence properties such as the nucleotide composition and the DNA shape are significantly linked with both types of pauses, and we also show that pausing sites can be predicted with high probability from DNA sequence features alone. These findings illustrate the importance of DNA sequence characteristics in the control of both promoter-proximal and gene-body pauses in human cells. In addition, the signal intensities of pauses in the promoter-proximal and gene-body regions are on average similar. Despite these similarities, we also observe differences between both classes of pauses. First, the DNA sequence underlying both types of pauses differs. Whereas a clear sequence motif was uncovered for pauses in the promoter-proximal region, no cis-elements are located strongly overlap with the region where promoter-proximal pausing of Pol II usually occurs. These findings extend previous observations that have linked core promoter elements with transcriptional pausing in Drosophila  are available at GEO with the accession code: GSE162857.Scripts for data processing and analysis are accessible through our GitHub site: gkab208_Supplemental_FilesClick here for additional data file.


These emVars are predicted to alter the binding motifs of important immune transcription factors, are enriched for associations with neutrophil and white blood cell count, and are associated with the expression of genes that function in innate immune pathways including inflammatory response and antiviral defense. We combined the MPRA data with other data sets to identify strong candidates to be driver variants of positive selection including an emVar that may contribute to protection against severe COVID-19 response. We endogenously deleted two CREs containing expression-modulation variants linked to immune function, rs11624425 and rs80317430, identifying their primary genic targets as ELMSAN1, and PAN2 and STAT2, respectively, three genes differentially expressed during influenza infection. Overall, we present the first database of experimentally identified expression-modulating Neanderthal-introgressed alleles contributing to potential immune response in modern humans.Although some variation introgressed from Neanderthals has undergone selective sweeps, little is known about its functional significance. We used a Massively Parallel Reporter Assay (MPRA) to assay 5,353 high-frequency introgressed variants for their ability to modulate the gene expression within 170 bp of endogenous sequence. We identified 2,548 variants in active putative  Toll-Like Receptor  and 292 variants that potentially modulate gene expression. This work represents the first high-throughput functional screen directly targeted at Neanderthal introgressed variants and reveals a set of directly identified putative causal variants, many of which display substantial additional evidence of affecting important immune pathways and being potential drivers of positive selection signals across the globe. Our results not only provide insights into the effects of adaptive archaic variation but are paramount toward a deeper understanding of the consequences of archaic introgression in modern humans.However, characterization of the functional consequences of introgressed genetic variation on the present-day human cells remains limited. Given that only a small portion of Neanderthal introgression affects protein coding sequences , a few sSince many putative adaptive introgressed variants show signs of local adaption , we aimeWe prioritized variants previously identified as introgressed using the Altai Neanderthal genome  that havWe employed a MPRA technique following We next tested these 11,792 total sequences in an MPRA following P value of less than 0.01, the element is considered “active”   . This transcript count was compared with the tested element’s representation in the DNA vector pool. If the log2-fold change (LFC) of this ratio significantly differed from 0, with an adjusted “active” , in that2×10−16)  online a2×10−16)  online. 2×10−16) B. MoreovP < 0.0001, FDR < 0.05) and promoter flanking regions  , which are indicative of nonactive genomic region   to interact with a promoter in one of three immune tissues . These results suggest that the MPRA CREs display properties consistent with activity in the endogenous K562 context and therefore can provide insights into immune biology.The MPRA discovered that 2,548 (48%) of tested elements are in active CREs in either the introgressed and/or nonintrogressed form  online.  regions  (OR = 1. < 0.05) A. These 62 cells . Furtherc region . Also cor = 0.97, P = 0.03) across these four sets between the percent DHS overlap and percent MPRA activity  as the introgressed (37.8%) and nonintrogressed (38.6%) CRE sets B. This sactivity  online. P value less than 0.01 . We refer to such variants as “expression-modulating variants” (emVars) and we identified a total of 292    online. P = 0.4) . As suppr = 0.69, P = 4.48×10−5, We found further support for the emVar data by conducting single-locus reporter assays (SLRAs). We conducted SLRAs on 14 tested sequences (0.26% of tested variants), exceeding the proportion tested in similar experiments  and K562 DHS sites   and strong enhancers , these latter two enrichments were not found within just active CREs. CREs with emVars were also depleted for heterochromatin chromatin marks . In addition, when considering the magnitude of expression modulation across genomic features, emVars within CREs that have chromatin marks consistent with strong enhancers modulated expression more strongly than those within CREs with weak enhancer chromatin marks, though this comparison did not pass multiple hypothesis testing correction   C. They wP = 0.013) (P = 0.041), and variants that are eQTLs in any tissue besides the three most relevant are not enriched for being emVars (P = 0.84) (P = 0.0014) . About 93 of these variants were emVars, which marks a significant enrichment for being emVars  C. Varian = 0.84) C, as exp 0.0014) C, althout-test, P = 0.038)   D. Finall= 0.034) E. We expP = 2.1×10−5) or active sequences with no emVar  (t-test P = 6.9×10−5)  (We further found concordance between emVars and TF-binding data. We used FIMO  to searc.9×10−5) A. Furthe.9×10−5) B. Finall 0.0004) C. OveralP-adj = 0.005) and is nearest to interferon regulatory factor 5 (IRF5) for which it acts as an eQTL (P = 2.9e-10). This gene codes for a TF active in both innate and cell-mediated responses and mutations in this gene are associated with autoimmune disorders such as inflammatory bowel disease  TF   and survived FDR correction (FDR<10%): TFEB (OR = 17.4), E2F3 (OR=10.5), SNAI1 (OR=10.0) CTCFL (OR = 9.76), NFIC (OR = 4.6), SP1 (OR 3.6). Several of these TFs have important immune functions. For example, TFEB, the TF for which emVars are most overrepresented, is a master regulator of lysosomal biogenesis and autophagy . We found 31 phenotypes for which at least one emVar was significantly associated (P < 10−7). We conducted an enrichment analysis and found four phenotypes for which emVars are significantly enriched  relative to tested non-emVars: 1) immature reticulocyte fraction, 2) neutrophil count, 3) white blood cell (leukocyte) count, and 4) noncancer illness code, self-reported: interstitial lung disease  genes and the neighboring FAM114A1 gene. Via the MPRA, we found support for rs73236616 as an emVar that may be driving positive selection at this locus . We provide these findings for functional follow-up studies for biomedical use  and six additional variants in moderate LD with the lead variant (r2 ≥ 0.5). Two of the strongly associated variants, both of which are eQTLs for OAS1 and OAS3 in multiple cell types , a pro-inflammatory cytokine (ASB5 (rs13129964:G:A) and FBXO7 (rs73170400:A:C), two genes in the MHC-mediated antigen processing and presentation pathway  , a TF expressed in a wide range of tissues  infection  A, which ropeans) . This haropeans)  online, ropeans) . The MPRividuals , and ELM tissues  that has tissues  and decr tissues . ELMSAN1nfection , and is ELMSAN1 in immune cells (ELMSAN1 which displayed a reduction in expression that trended toward significance with the larger deletion (LFC = −0.89 and P = 0.064) and was significant with the smaller targeted emVar deletion (LFC = −1.61 and P = 0.007)   of emVar rs11624425, we used combinations of CRISPR-Cas9 sgRNA vectors to excise from the endogenous genome of K562 cells a 261-bp region containing the MPRA-tested 170-bp CRE and a smaller 22-bp region just surrounding rs11624425. We then used qPCR to determine the effect that these deletions had on the expression of putative response genes relative to a housekeeping gene . Consistent with Hi-C interaction data showing an interaction between this variant and the promoter of ne cells , the mos= 0.007) D and E. = 0.036) E, though= 0.036) B and C, eriments  demonstreriments  is biocheriments . This suP = 0.007; SLRA LFC = −0.76 P = 1.3×10−10, IL23A, a pro-inflammatory cytokine, STAT2, a critical part of the interferon immune signaling response for which our emVar set is enriched (see above), and PAN2, a part of the poly(A)-nuclease deadenylation complex which deadenlyates mRNA leading to its degradation  and, to a lesser degree, STAT2 expression   I and J iSTAT2 and PAN2 are upregulated in response to IAV infection   , an alleDNA sequence analysis has shown that modern and archaic humans interbred. Since then, one of the big remaining mysteries is characterizing the function and impact of archaic alleles in modern humans. Here, we identified 292 Neanderthal introgressed alleles that have reached appreciable frequencies in modern human populations, and which show direct evidence of having a regulatory effect in K562 cells. We also identified 2,548 such variants that fall within active CREs in this cell line. By specifically investigating the effects of each Neanderthal allele independently, we are able to identify putative driver variants of positive selected introgressed haplotypes to connect putative drivers to changes in gene expression, TF-binding and phenotypic associations. This work adds a useful data set to a growing effort to tease out the effects of introgressed variation on gene expression and phenotypic variation and to disentangle the phenotypic contribution of introgressed variation from linked human alleles .MPRA experiments are becoming a new tool in the effort to use genetics to understand evolutionary questions. For example, MPRAs have been conducted looking at the effects of fixed differences between humans and Neanderthals/Denisovans  as well There are some notable limitations with MPRA in general as well as with our analyses specifically. For example, MPRA is prone to many false negatives for a variety of reasons including that variants may have expression modulating potential in a cell type not tested, and/or that such variants may reside in a CRE larger than the tested element size. Furthermore, because we used an episomal reporter vector, which has been shown to be somewhat less accurate than integrated reporter assays , this maDespite these limitations, we found that these introgressed emVars are enriched for signals of endogenous function, including overlaps with chromatin mark data and enrichment in Hi-C interactions. We further found that emVars are enriched for altering important immune TF-binding sites, are enriched for significantly associating with neutrophil and white blood cell counts, and are associated with the expression of genes that act within important immune pathways including innate immune response and antiviral response.OAS1-2-3, including one emVar identified in the MPRA, have been found to be somewhat protective against severe COVID-19 response  in any tissue . Additior2 ≥ 0.9). This sequence is then considered as the “LD background” for the variant. There were 450 such cases in the data set accounting for 8.4% of oligo sequences. Similarly, in the nonintrogressed vector, if a nonreference allele in the flanking region is linked (r2 ≥ 0.9) in Africans to the nonintrogressed allele, the linked allele is used in the oligo. Additionally, on both sides of the oligo, 15 bp of adaptor sequence was included to facilitate cloning into the MPRA vector. All cloning steps to create the MPRA library followed Following 2. Total RNA was extracted and GFP mRNA was isolated and prepared for sequencing following For K562 cells, for each of four replicates, 60 million cells per replicate were transfected with 60ug the MPRA library using electroporation with the Neon transfection system following the manufacturers protocol. About 24 h after transfection, cells were collected and snap frozen in liquid NAll MPRA data analysis followed The 150-bp paired end reads from the sequencing of the mpraΔorf library were merged using Flash v.1.2.11 . Merged amplicon sequences were then filtered for quality control such that sequences were kept if: 1) there was a perfect match of 10 bp on the left or right side of the barcode, 2) the 10 bp on both sides of the barcode matched with levenshtein distance of 3 or less, and 3) the 2 bp on either side of the barcode matched perfectly. Sequences that passed through these filters were aligned back to the expected sequence pool using Bowtie2 v. 2.3.4.1 with the –very-sensitive flag. Alignments that had less than 95% perfect matching with the expected sequence and any alignment which had a mismatch at the variant position were removed. Barcodes that matched to more than one expected sequence are unusable and therefore were also removed.We identified an average of 13,513 unique barcodes per oligo in the plasmid sequencing and all oligos were represented in the barcode-tag sequencing. This high number of barcodes is beneficial because it represents more unique molecules throughout the entire MPRA process, including reduced PCR bias in the presequencing amplification steps. Furthermore, because, as described below, our workflow involves summing the counts across all barcodes for a single oligo sequence, a large number of different barcodes leads to reduced impact of a biologically impactful barcode affecting the transcript count.Again following r > 0.99, P < 2.2e-16 across all pairwise replicates; P value of less than 0.01 for a significant result. If an oligo sequence had a significant LFC with either allele, the sequence is considered “active.” As further assurance as to the precision of this measurement, we examined the ratio of cDNA counts to pDNA counts across all the replicates and again found extremely high magnitude and significant correlations , for oligos which were determined to be active, we used Deseq2 to calculate the fold change between the two versions of the oligo sequences with Wald’s test to calculate the A, r = −0.21, P < 2.2e-16), with the majority of oligos having a relative low CV , overall mean = 0.38, and median = 0.21. Importantly, as described above, Deseq2 appropriately accounts for dispersion, as oligos with higher CV require greater magnitude LFC cDNA:pDNA to achieve the P value for significance   for the normalized transcript count for every oligo and how CV is related to number of barcodes as well as how it affects the likelihood of being determined by Deseq2 as active and expression modulating. As shown in supplementary fig. 6P value P < 10−4; if the other sequence had a least a P value P < 10−3, we computed the differential predicted binding score by subtracting from the score of the introgressed allele the score for the nonintrogressed allele (introgressed score–nonintrogressed score). If the other allele did not have a score P < 10−3, we conservatively did not include this motif in the analysis because we cannot be confident as to the binding difference. For each pair set, if multiple significant differences existed, we chose the motif with the highest absolute predicted binding difference. For the enrichment plot in P values were corrected for multiple hypothesis testing using the Benjamin–Hochberg method.For the TF-binding analysis, we downloaded the entire set of the high-quality “core collection” human transcription-binding motifs from HOCOMOCOv11 . We trimhttp://www.nealelab.is/uk-biobank/). To reduce the number of tests, we downloaded a subset of relevant phenotypes (shown in P < 10−7) and compared the number of emVars with non-emVars using Fisher’s exact test. P values were corrected for multiple hypothesis testing using the Benjamin–Hochberg method. Overlaps with positive selection windows were performed using https://atlas.ctglab.nl/PheWAS, last accessed November 19, 2021). These instances are noted in the text.UKBiobank GWAS data were downloaded from the round 2 Neale Lab results  from Hi-C experiments on lymphoblasts, spleen, and thymus  using a random number generator and then input these 100 sets into Reactome. We then removed any enrichments that were significantly enriched in five or more random subsets for an FDR <5%.To identify promoters physically interacting with emVars or the CREs containing emVars, we intersecting the 170-bp CREs containing emVars with significant promotor–DNA interactions . Once cloned, vectors were transfected into NEB5 alpha E coli. and grown to transfection concentration in 150 ml LB for 9 h. Vectors were isolated using Zymo Maxi Prep kits. For each replicate, 20ug of each vector in the desired guide combination were transfected into 10 million K562 cells using the Lonza Nucleofection system. In addition, for each replicate, 10 million cells were also transfected with 40 μg of the empty Px458 vector as a control. About 48 h later, experimental and control cells were collected and FACs sorted for GFP. About 1×105 of the sorted cells for each sample were used to collected gDNA using the Omega E.Z.N.A Tissue DNA kit. Deletions were confirmed via gel or amplicon sequencing depending on the size of the deletion. Total RNA was collected from the remaining cells using the Qiagen RNAeasy mini kit. cDNA was synthesized using the Superscript III kit and qPCR was performed in triplicate. Expression was normalized to the PGK1 housekeeping gene using the ΔΔCt method. qPCR values are reporter as the fold change of the normalized expression of the gene in the deleted cells relative to the normalized expression of the empty CRISPR vector transfected cells.Guides were designed using the Broad Institute GPP sgRNA Designer. Forward and reverse guides were ligated and cloned into the Px458 CRISPR vector following . pSpCas9Molecular Biology and Evolution online.msab304_Supplementary_DataClick here for additional data file.


Transcription and replication are the two most essential processes that a cell does with its DNA: they allow cells to express the genomic content that is required for their functions and to create a perfect copy of this genomic information to pass on to the daughter cells. Nevertheless, these two processes are in a constant ambivalent relationship. When transcription and replication occupy the same regions, there is the possibility of conflicts between transcription and replication as transcription can impair DNA replication progression leading to increased DNA damage. Nevertheless, DNA replication origins are preferentially located in open chromatin next to actively transcribed regions, meaning that the possibility of conflicts is potentially an accepted incident for cells. Data in the literature point both towards the existence or not of coordination between these two processes to avoid the danger of collisions. Several reviews have been published on transcription–replication conflicts, but we focus here on the most recent findings that relate to how these two processes are coordinated in eukaryotes, considering advantages and disadvantages from coordination, how likely conflicts are at any given time, and which are their potential hotspots in the genome. Transcription is the process that produces RNA using DNA as a template. This allows cells to express the functional relevant parts of their genome and consents each cell in an organism to acquire specific functions. The diversity of transcripts that each cell can create permits the generation of 200 different cell types in the human body, all with virtually identical genomes. Importantly, this large variety of transcripts can be produced by different RNA Polymerase complexes, each responsible for a specific subset of transcripts :RNA Polymerase I (RNAPI) transcribes the ribosomal RNA , transcribed as a single polycistronic RNA subsequently processed in single rRNAs; ribosomal RNA transcripts are arranged in rDNA clusters present in the short arms of five human chromosomes , in a broad range of copies of each unit per clusters [RNA Polymerase II (RNAPII) transcribes messenger RNA (mRNA) from genes, approximately 42,000 in total in the human genome, half of which are protein coding and the other half noncoding [RNA Polymerase III (RNAPIII) transcribes transfer RNA (tRNA), with approximately 500 present in the human genome ; RNAPIIIIn reality, recent evidence has shown great crosstalk between the different complexes, meaning that the distinction between the roles of each complex is less neat than previously thought. For example, for a long time, it was known that RNAPII and RNAPII-associated transcription factors are present next to sites of RNAPIII transcription ,5,6. MorRNA polymerases complexes have molecular weights of more than 500 kDa even without accessory transcription factors, and as such, are much bigger than the physical barriers that replicative helicases can overcome on DNA . ConsequThere are several mechanisms through which transcription can affect directly or indirectly DNA replication progression inducing genome instability :(i) Increased formation and/or persistence of three-stranded RNA–DNA hybrid structures called R-loops, formed when the nascent RNA hybridises back with the template DNA strand displacing the non-template DNA strand ,27; R-lo(ii) Accumulation of positive and negative supercoiling that induces increased topological constraints ,32,33.(iii) Accumulation of stalled/paused/backtracked RNA polymerase  ,34.(iv) Increased occurrence of DNA damages at transcribed regions ,36,37.Nevertheless, the distinction between these mechanisms is not neat, and for example, impairments of topoisomerases and transcription stress can likewise lead to increases in R-loops levels ,38,39. AS. cerevisiae that tRNAs act as hotspots where replication fork stalls and pauses [One simple possibility would be to keep the time when replication occurs in a cell separate from when the cell transcribes. It was known and thought for a long time that DNA replication is restricted exclusively to the S-phase of the cell cycle, and only once completed can cells progress into mitosis ; howeverd pauses . Finallyd pauses ,65. Gened pauses . ConsideAn additional layer of complexity comes from the analysis of DNA replication timing and the distribution of replication origins. Several studies across many model systems have invariably shown that transcribed regions are preferentially replicated in the early S-phase, while poorly transcribed regions are preferentially replicated in the late S-phase ,68,69. CWang et al. and the St Germain et al. papers show that hotspots of G-MiDS and TRI correlate with genomic sites frequently rearranged and mutated in tumours, linking once more transcription and replication conflict regions to hotspot sites of genome instability linked to human diseases [Some evidence in support of such an arrangement comes from studies analysing the nuclear distribution of active replication and active transcription throughout the S-phase. Some data show that transcription and replication occur in different parts of the nucleus throughout the S-phase, suggesting that when a region is replicated, it is not at the same time also transcribed ; howeverdiseases ,78. AltoCan we, therefore, evaluate this problem from an evolutionary point of view, assessing perhaps what would be the best alternative for a cell in an ideal scenario? Practically analysing this situation, it would be convenient for a cell to keep the two processes separated, as interference between transcription and replication has been widely associated with increased DNA damage and genome instability. In support of this view, we have the fact that transcriptional defects drive directly increased DNA damage in cells ,34, manyHowever, again analysing the evidence hypothetically, there are benefits for a cell in having the two processes coexisting. It would be easier to load and activate replication initiation complexes in the open chromatin conformation of transcribed regions  70,71,7,73. MoreFollowing on from the above speculation, there appear to be more advantages in not coordinating transcription and replication than in coordinating the two processes. Perhaps a clearer evaluation of this conundrum can come from assessing how many transcripts are produced by the transcription machinery at any given time, and the broader impact of transcription on genome biology. As mentioned at the beginning, the three RNA polymerases are together responsible for transcribing thousands of different transcripts. Moreover, it is also clear that most of the transcription in terms of the number of different transcripts is performed by RNAPII, which also has its transcripts interspersed in the whole genome . HoweverWhat these numbers indicate is that perhaps the two processes of transcription and replication do not need to be coordinated, as the low number of RNAPII complexes present in a gene by itself reduces the risk of a collision at any given time. Moreover, they can explain the contrasting findings mentioned earlier, such as the overlap and coordination data obtained by immunofluorescence and genome-wide analyses ,76,77,78Whether components of the transcription machinery will remain on chromatin between ‘on’ bursts is not clear. If so, it is the promoter region that could represent a hotspot for high-risk transcription–replication conflicts, because of the high density of proteins present there  106,107107. AmonPerhaps here is a key aspect in understanding the relationship between transcription and replication: cells need to maintain RNAPII near the TSS to maintain the correct chromatin structure of TSSs that will allow them to transcribe genes when needed. This chromatin organisation is also highly conserved during DNA replication, with nucleosome positioning maintained around the TSS also in newly replicated chromatin . AltogetIt has been long known that TSSs and promoters represent hotspot sites of genome instability and DNA damage ,115,116.In the future, it will be important to determine what factors and pathways are involved in the coordination between transcription and replication when these two processes are close together, but also defining whether there is an increased risk of genome instability every time transcription and replication collide. This may depend on many factors such as on the directionality between transcription and replication, affected by the local chromatin environment and histone marks, the RNAPII status, or ultimately whether these collisions will impair the ability to progress of either the RNAPII or the replisome.


Geminiviruses are plant DNA viruses that infect a wide range of plant species and cause significant losses to economically important food and fiber crops. The single-stranded geminiviral genome encodes a small number of proteins which act in an orchestrated manner to infect the host. The fewer proteins encoded by the virus are multifunctional, a mechanism uniquely evolved by the viruses to balance the genome-constraint. The host-mediated resistance against incoming virus includes post-transcriptional gene silencing, transcriptional gene silencing, and expression of defense responsive genes and other cellular regulatory genes. The pathogenicity property of a geminiviral protein is linked to its ability to suppress the host-mediated defense mechanism. This review discusses what is currently known about the targets and mechanism of the viral suppressor AC2/AL2/transcriptional activator protein (TrAP) and explore the biotechnological applications of AC2. Geminiviridae constitutes the largest family of plant viruses with nine genera and 485 species  of ~200 nucleotides. An inverted repeat within the CR forms a hairpin loop, and within the loop is the conserved 9-nt sequence 5'-TAATATT↓AC-3'.Geminiviruses are single-stranded (ss) DNA viruses that cause major losses to a number of economically important crops throughout the world . Geminiv species . Geminiv species . The virBecurtovirus, Begomovirus, Capulavirus, Curtovirus, Eragrovirus, Grablovirus, Mastrevirus, Topocuvirus, and Turncurtovirus, based on the genome organization, host-range, and the type of insect vector which transmits the virus , Beet severe curly top virus (BSCTV), and Spinach curly top virus (SCTV) exhibits very little sequence similarity when compared to begomovirus AC2/C2 except for the conserved Cys-His residues in the middle . AC2 is a delayed early gene product which transactivates late viral genes,  and NSP , suppres and NSP , and act and NSP . C2, a pfunction . The C2 e middle . AC2 hase middle . (2) A ce middle . This reArabidopsis -infected Arabidopsis are RDR1/2/6-independent primary siRNAs. Interestingly, RDR6 was shown as a target of Mungbean yellow mosaic India virus (MYMIV) AC2  AC2 . AlthougMIV) AC2 ,B. Abundus ACMV; . Prevale TYLCSV;  and prefTYLCCNV; . InteresTYLCCNV;  are the h CaLCuV .de novo methylation of geminiviral genome  of cytosine residues and histone H3 lysine 9 dimethylation (H3K9me2) of chromatin are hallmarks of epigenetic defense mechanism evolved by the plants against invading DNAs including geminiviruses and transposons. The equilibrium between repressed and active viral chromatin determines the outcome of infection and symptom remission. Host recovery is tightly associated with the equilibrium favoring repressed state . The canl genome . Arabidoansposon  was evokl genome . A studyThe presence of a robust viral counter defense mechanism is underscored by the ubiquitous presence of one or more silencing suppressor proteins in the armor of a virus. The arms race between silencing and silencing suppression results in resistance or susceptibility to the pathogen. Geminiviruses encode several proteins namely AC2/C2, AC4/C4, AV2/V2, βC1, and Rep that suppress RNA silencing by targeting various components of the post-transcriptional gene silencing (PTGS) machinery, transcriptional gene silencing (TGS) machinery, and cellular regulatory genes (reviewed in AC2 of begomoviruses and C2 of curtoviruses act as suppressors of silencing through two broadly classified mechanisms: (i) activation domain-dependent silencing suppression- begomovirus AC2 with a C-terminal acidic activation domain  is involN. benthamiana line 16c, in which the previously established silencing of gfp was reverted by the expression of the ACMV AC2 in a potato virus X (PVX) vector. Similar studies with C2 of Tomato yellow leaf curl virus-China (TYLCV-C), a monopartite begomovirus, revealed that the suppression of silencing was associated with the presence of an intact zinc finger-like motif (East African cassava mosaic Cameroon virus (EACMCV) and Indian cassava mosaic virus (ICMV) are known to suppress silencing (Sri Lankan cassava mosaic virus (SLCMV), which exhibit recovery from symptoms 2 to 3 weeks after infection (Mungbean yellow mosaic virus (MYMV) AC2 in addition to the NLS and Zn-finger motifs to suppress silencing. The need for NLS, Zn-finger motif, and activation domain of AC2 for efficient silencing suppression suggested that MYMV and ACMV AC2 and TYLCV-C C2 regulate silencing suppression in the host cell nucleus and are dependent on DNA interaction and transcriptional activation.Suppression activity of AC2 was first demonstrated by ke motif  and an Nke motif . East Afilencing . Althougnfection . The actnfection . Trinks AC2 in Arabidopsis protoplasts upregulated the expression of 30 plant genes. One such gene was Werner’s exonuclease-like 1 (WEL1), a homolog of Werner Syndrome-like exonuclease (WEX). WEX, an RNase D exonuclease-like protein acts as a positive regulator of post-transcriptional gene silencing  AC2 induces a calmodulin-like protein Nb-rgsCaM  displayed higher pathogenicity in comparison to ICMV-Dharwad (ICMV-Dha). A single point mutation that changes Tyr to Cys in ICMV-Dha AC2 (ICMVDhaY11C) significantly increased pathogenicity  and CML38 (rgsCaM homolog) was observed in TuMV HC-Pro transgenic lines  or infection of SAMDC1 mutant plant with wild-type BSCTV strain resulted in enhanced DNA methylation, reduced viral titer and reduced susceptibility confirming the key role of C2 and SAMDC1 in regulating host DNA methylation. Agroinfiltration of BSCTV C2 and SAMDC1 genes reverted gfp silencing in N. benthamiana 16c (BSCTV C2 interaction with S-adenosyl methionine decarboxylase1 (SAMDC1) and resultant suppression of bidopsis . SAMDC1 bidopsis . Hence, bidopsis . The resiana 16c .Arabidopsis mutants adk, cmt3, drm1/2, drb3, clsy1, pol IV, and pol V (mutants of RNA-directed DNA methylation components) were hypersensitive to viral infection. Cytosine methylation level of the viral genome was significantly reduced in the hypersusceptible mutant plants (Arabidopsis with a BCTV mutant lacking C2 (BCTV C2−) resulted in a host recovery phenotype. The viral DNA in the recovered tissue was hypermethylated, suggesting methylation of the viral genome to be the cause of host recovery. Interestingly, ago4, dcl3, drb3, and pol V mutant Arabidopsis plants did not recover when infected with BCTV C2−  line, in which the 35S promoter-driven gfp transgene is transcriptionally silenced. The silencing of the 16-TGS plant was suppressed by wild-type AC2 and AC2-∆AD of TGMV and CaLCuV and also by BCTV C2 when expressed from PVX vectors -inducible promoter reversed the methylation of transcriptionally silenced loci in Arabidopsis. The reversal of methylation by AC2 and AC2-∆AD of TGMV, and BCTV C2 was found to be locus non-specific. Four independent regions, including one gene-coding region, two transposable elements, and one repetitive DNA region were used as markers to study the effect of AC2 and AC2-∆AD of TGMV, and BCTV C2 on cytosine methylation. Reversal of cytosine methylation resulted in ectopic expression of TGS-silenced loci namely, a putative F-box gene, a retrotransposon AtSN1 (SINE element), and Athila (LTR element) in all analyzed transgenic plants. The ability of AC2 and AC2-∆AD of TGMV and BCTV C2 to suppress TGS indicates that the suppression is activation domain-independent. TGS in the CACTA-like transposon was reversed only upon wild-type AC2 induction. This invoked the necessity of transcriptional activation domain for mediating suppression of the CACTA TGS-loci. Thus, AC2 transgenic plants reduced cytosine methylation of a wide spectrum of genomic regions and reversed methylation of the TGS loci in Arabidopsis by non-specifically inhibiting cellular transmethylation reactions  caused a delay in flowering , the authors serendipitously observed accumulation of the NC2 transcript in addition to the Rep transcript in one of the transgenic Arabidopsis lines. Additional transcription from the C2-C3 promoter was correlated with hypomethylation of the C2-C3 promoter. Ectopic vegetative transcription of the endosperm-imprinted E3 ubiquitin ligase-encoding gene VARIANT IN METHYLATION5 (VIM5) was observed only in the NC2 transcript-expressing transgenic line, when compared to wild-type Arabidopsis. The role of VIM5 in hypomethylation was substantiated by over-expressing VIM5 in another Rep transgenic line that did not originally accumulate the NC2 transcript; Over-expression of VIM5 triggered accumulation of the NC2 transcript which correlated with decreased DNA methylation at the transgenic C2-C3 promoter. Infection with BSCTV resulted in transient expression of the host endosperm-imprinted E3 ubiquitin ligase-encoding gene VIM5 in rosette leaf tissues. BSCTV infection of Arabidopsis vim5 mutants resulted in delayed accumulation of viral early gene transcripts C2 and C3, thus confirming the role of VIM5 in contributing to the early expression of C2 and C3 from the viral genome. VIM5 was shown to interact with host CG MTase MET1 and the CHG MTase CMT3 and promote 26S proteasomal degradation of MET1 and CMT3. MTase mutant plants met1 and cmt3 displayed reduced viral methylation at the C2-C3 promoter. Similarly, infection of a modified BSCTV with substitutions at CG and CHG sites in the C2-C3 promoter showed higher viral accumulation in vim5 mutant when compared with the unmodified BSCTV. Thus, early expression of BSCTV silencing suppressor C2 and replication enhancer protein C3 immediately after the expression of Rep, by activating an imprinted E3 ubiquitin-ligase gene, is a strategy evolved by the virus to inhibit viral DNA methylation and establish disease  of CaLCuV AC2 protein. A phosphomimic mutation of the S109 reduced viral DNA accumulation and delayed symptom appearance in Arabidopsis, thus revealing phosphorylation of viral protein as a host defense mechanism against an invading virus , a serine-threonine kinase, and inactivate it . SnRK1 hng virus . Guerrerof SnRK1 .Arabidopsis increased the cellular cytokinin levels, substantiated by increased activity of a cytokinin-responsive promoter. Over-expression of CaLCuV AC2 and SCTV C2 in Arabidopsis resulted in increased expression of endogenous cytokinin-responsive promoters. Thus, geminivirus AC2, by inactivating ADK, increased the cellular cytokinin levels. An enhanced cytokinin level is a prerequisite for cell cycle progression and to maintain an active state of replication. Increased cytokinin resulted in an enhanced susceptibility phenotype. Interestingly, cytokinin is known to negatively regulate the expression of SULTR1;2, a high affinity sulfate transporter in Arabidopsis roots (Arabidopsis histidine kinase 4 (CRE1/WOL/AHK) is the cue for negative regulation of sulfur assimilation, while cre1 mutant is insensitive to cytokinin. In sultr1;2 mutant, SULTR1;1 was downregulated by cytokinin. Downregulation of sulfate transporters by cytokinin correlated with the decrease in sulfate uptake. Another study by APR1) and SULTR1;2 through a pathway independent of sulfur starvation; likely through increasing sucrose concentration which is known to upregulate APR1 gene (is roots . CytokinPR1 gene . ExogenoPR1 gene .C2 transgenic plants repressed the expression of three genes involved in sulfur assimilation namely ATP sulfurylase 3 (APS3), APS reductase 1 (APR1), and APS reductase 3 (APR3) and also accumulated reduced sulfur (Tobacco mosaic virus (TMV) symptom development through a phenomenon named sulfur-induced resistance (SIR) or sulfur-enhanced defense  reversed the repression of sulfur assimilation genes . TYLCV C2 and BCTV C2 were also shown to interact with CSN5 suggesting that CSN5-C2 interaction is a conserved function in geminiviruses , a highly conserved protein complex with eight subunits that resembles the 19S lid of the 26S proteasome , regulatn of CRL . Rubylativiruses .Pseudomonas syringae pv. tomato DC3000 (Pto DC3000). Coronatine application on plants facilitates stomatal opening and increases infection  C2 were shown to interact with RPS27A, a fusion protein consisting of ubiquitin at the N terminus and ribosomal protein S27a at the C terminus. C2 interaction was with the ubiquitin moiety of RPS27A. Jasmonic acid, which is involved in biotic and abiotic stress responses, is also known to induce the production of secondary metabolites including alkaloids, anthocyanins, and terpenoid compounds . Whitefltivation . TYLCV aArabidopsis PEAPOD2 transcription factor (TIFY4B) was shown to interact with AC2 and promoter sequences of TGMV and CaLCuV CP , the type member of Grablovirus, is known to be a serious threat to the Californian wine industry. Understanding the GRBV suppressor proteins and their plant targets can present cogent strategies for mitigating this threat to a multibillion-dollar industry. Most of our current knowledge on silencing and silencing suppression is from dicot plants Arabidopsis and N. benthamiana. The genus Mastrevirus are monocot-infecting geminiviruses with 41 species. The only silencing suppressor protein identified till date in Mastrevirus is the Rep protein from Wheat dwarf virus which binds to ss- and ds-siRNA (Mastrevirus.This review highlights the different targets and mechanisms evolved by geminivirus AC2/C2 to counter PTGS and TGS. Recent studies on cellular proteins that are targets of AC2/C2 have opened a third defense mechanism which does not involve PTGS and TGS . Many stds-siRNA . It woulAbutilon mosaic virus (AbMV) AC2 was shown to act as a brake in geminivirus replication and was reported to enhance PTGS rather than suppress it  has evolved an ability to interact with so many different targets and make an impact on viral pathogenesis and plant metabolism. It would be interesting to know which of the studied target/targets is relevant in viral pathogenesis. Editing of the target genes in plants and study of viral pathogenesis and RNA silencing in the mutant plants infected with viruses will help in a more thorough evaluation of the AC2-interacting plant proteins. press it . It woulcis or in trans have found application in biopharming (Bean yellow dwarf virus (BeYDV), a mild dicot infecting Mastrevirus, has been deconstructed and used widely as a replicon vector  p19, a ds siRNA binding suppressor protein. Earlier work by AC2, which is known to target multiple steps of gene silencing, could find extensive use for over-expressing recombinant proteins in plants and plant cell cultures.One major focus of future study should be in exploiting AC2 silencing suppressor function for biotechnological applications. The toxic effect of AC2, when expressed as a transgene, has been reported by several groups. This has limited the use of geminivirus AC2 in transgenic technology. Mutations of functional domains have been shown to alleviate the toxic effect . A studypharming . Bean yen vector . Huang eCP can be replaced with the gene of interest without affecting the systemic movement of virus (only in certain viruses), the recombinant vector derived from replacing CP is not vector transmissible and the broad host range of DNA viral vectors makes DNA viruses a good choice for VIGS over RNA viruses (Both RNA and DNA viruses are widely used in Virus-Induced Gene Silencing (VIGS). The ease with which DNA viruses can be manipulated, the fact that geminiviral  viruses . While s viruses , the pre viruses . However viruses . Hence, 1 generation and T-DNA segregated events in T2 generation. By co-expressing p19 as part of the CRISPR/Cas9 cassette, the authors could group the T1 plants based on the severity of p19-induced leaf phenotype alteration. T1 plants displaying severe phenotype associated with p19 over-expression were selected as edited lines. T2 seedlings from the gene edited T1 events were again grouped based on the leaf phenotype. Segregated plants with wild-type leaf phenotype were then identified as gene-edited plants from which the T-DNA was segregated out. The geminiviral suppressor protein AC2 can be co-expressed to increase the genome editing efficiency. Interestingly, AC2 is more attractive than TBSV p19 for the following reasons: (1) AC2 over-expression is known to cause pronounced phenotype in leaf  TGM in leaf . (3) AC2 in leaf , and henn AtDMC1 . Thus, fSS: conceptualization and writing-original draft preparation. KV: conceptualization, reviewing, and editing. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.