Further, we summarize possible ROS- and RNS-dependent sensors involved in metal stress sensing, such as kinases and phosphatases, as well as some ROS/RNS-regulated transcription factors that could be involved in metal homeostasis. Understanding ROS- and RNS-dependent signaling events is crucial to designing new strategies to fortify crops and improve plant tolerance of nutritional imbalance and metal toxicity.Deterioration and impoverishment of soil, caused by environmental pollution and climate change, result in reduced crop productivity. To adapt to hostile soils, plants have developed a complex network of factors involved in stress sensing, signal transduction, and adaptive responses. The chemical properties of reactive oxygen species (ROS) and reactive nitrogen species (RNS) allow them to participate in integrating the perception of external signals by fine-tuning protein redox regulation and signal transduction, triggering specific gene expression. Here, we update and summarize progress in understanding the mechanistic basis of ROS and RNS production at the subcellular level in plants and their role in the regulation of ion channels/transporters at both transcriptional and post-translational levels. We have also carried out an  ROS and RNS are involved in nutrient and heavy metal stress sensing, signaling, and adaptive responses. They act as important hubs in the regulation of expression levels and functionality of ion channels/transporters. Currently, more than a billion people suffer from malnourishment, while a similar number lack basic micronutrients, such as Zn, Fe, and Cu, in their diet. These deficiencies have a major impact on human health, and forecasts predict that this impact will become worse in the future , which accumulate as a result of either natural causes (e.g. rock weathering) or inappropriate agricultural practices such as the use of low-quality water for irrigation. Globally, the area of soil affected by salinity is increasing at an alarming rate of 2–3 ha minAn efficient way to deal with heavy metal contamination and salinity would be to develop crops that are able to take up significant quantities of heavy metals or salts from the soil without a yield penalty and, at the same time, prevent their accumulation in the edible plant parts. To achieve this aim, a deep understanding of the mechanisms that regulate the uptake, translocation, and sequestration of salt and heavy metals is required.·OH, H2O2, O2·–, and 1O2. These ROS originate as by-products of aerobic metabolism and their accumulation is determined by the balance between their production and their elimination by antioxidant systems (2O2) also have an important signaling role in the control of processes such as growth, development, or the response to different biotic and abiotic stress conditions  and various hormones , they increase their production of reactive oxygen species (ROS) such as  systems . UncontrIn the past years, an important role of ROS and NO in the regulation of ion channels and transporters of macro- and micronutrients as well as heavy metals has emerged Chlamydom·OH), are centers of molecular damage (–) (S-nitrosoglutathione (GSNO) (S-nitrosylation of the cysteine residues of proteins (S-nitrosylation is the enzyme S-nitrosoglutathione reductase (GSNOR)   Fig.1),,·OH), armage (–) . On the proteins . The masresidues  of proteresidues . Furtherhttps://www.psb.ugent.be/webtools/ptm-viewer/index.php) resulted in 1317 proteins in Arabidopsis thaliana being the target of methionine oxidation, of which only five were identified as transporters; 3438 proteins that are targeted for reversible cysteine oxidation, five of which were identified as transporters; and 6836 proteins that can potentially experience sulfenylation, of which only 11 were identified as transporters and only two were related to metal transporters  and micronutrients  is mediated by transporters and channels located at the plasma membrane and tonoplast. Some of these are highly selective for specific ions whereas others may allow the passage of a large number of ions. In addition, some of them may share transcription factors or certain essential components of the signaling network that regulates their expression . The aboransport . To get 2+, Fe2+, Zn2+, Cu2+, Cd2+, Al3+, Co2+, and Ni2+ in prokaryotic and eukaryotic organisms , the most important sources of ROS associated with the plasma membrane  genotype   are an evolutionarily conserved family of proteins that function as proton-coupled metal ion transporters that can transport Mnrganisms  and func network , like Atthe cell . Severalmembrane , showed nic (As) . Additioa2noa1-2 , and NRAanalysis .Hordeum vulgare  family  belongs to the IRT family . Zn defi vulgare . Overexpescribed . IRT1 rea2noa1-2 . In accoegulated , probabla2noa1-2 . On the analysis .+-dependent antiporters and are involved in fungi and plants in preventing the negative effects of Fe2+ excess (2+ and Zn2+ into the vacuole (2+ and Mn2+ (2+-sensitive cross-complementer 1 (Ccc1) is the VIT1 ortholog in yeast, and it has been suggested that ROS can increase the activity of Ccc1 transporter  family are tonoplast-localized transporters. They probably function as H+ excess . In ricea2noa1-2 . All the2+ uptake and homeostasis, and are localized in the plasma membrane of root tip cells  was identified  play key roles in heavy metal transport and detoxification in plant cells. HMPs are metalloproteins or metallochaperone-like proteins containing heavy metal-associated (HMA) domains with two cysteine residues that bind and transfer Cu, Cd, Co, Zn, and other heavy metal ions . In Arabe mutant . On the ve tools . In turnnfidence . AdditioOphiopogon japonicus , reducing ROS levels, in turn leading to increased expression of ABCG36, which finally reduces Cd accumulation. Furthermore, Arabidopsis AtABCG40/PDR12 is located at the plasma membrane and is strongly induced by Pb2+ treatment, and its overexpression leads to plants being more Pb2+ tolerant due to Pb2+ efflux , with NO reducing the activity, although the underlying mechanism is not well known  to and from cells, mainly into the vacuole . As an epiration . In wheae domain . As ment at Y891 . Further2+ and Cd2+. AtCAX3 has been shown to be involved in the efflux of Ca2+, Zn2+, and Cd2+ in Arabidopsis plants  family comprises vacuole-localized transmembrane antiporters that use secondary active transport to exchange cations from the cytoplasm with protons, transporting cations to the vacuole to maintain ion homeostasis in guard cells , which are cation-selective channels that have a high ion conductance for both monovalent (K+) and divalent (Ca2+) cations, can be regulated by oxidation and S-nitrosylation (S-nitrosylation and disulfide cross-linking (2+-ATPase (SERCA) can also be modified by cysteine oxidation or tyrosine nitration, while plasma membrane Ca2+ ATPase (PMCA) is inhibited either by its direct oxidation or by methionine oxidation in its binding partner calmodulin . These NSCCs are permeable to a wide range of cations, including essential macronutrients  and micronutrients   and ionotropic glutamate receptors  (2+-permeable) are also classified as NSCCs  , as well0 genes) . In addi2O2 and for ·OH. In root epidermis, ·OH activates NSCCs, triggering a simultaneous Ca2+ influx and K+ efflux ; this activation has been observed in both the mature root zone and the root apex of a large number of plants  and root epidermis, and are known to be activated by ·OH. Discovered first in Arabidopsis  treatment, and +-selective channels in guard cells may be inhibited by H2O2  lacked ·OH- activated Ca2+- and K+-permeable conductance in root epidermis. Thus, at least two mechanisms seem to coexist and act in concert to amplify ·OH-induced K+ efflux.Some reports on guard cells have shown that both inward- and outward-rectifying K2O2 stimulates anion efflux in cultured Arabidopsis cells  in wheat. The activity of mitochondrial K-ATP+ in pea was also modulated by NO and H2O  channels encoded by the TPC1 gene by physiologically relevant concentrations of H2O2 has also been demonstrated in direct patch-clamp experiments  showed increased accumulation of ROS under salt stress  was shown to be down-regulated under Fe deficiency  that could be oxidized  in mice , a key regulator of the Cys/Arg branch of the N-degron pathway  in proteins. ROS and NO interact both with each other and with proteins involved in their production and metabolism, regulating their own levels and the cellular redox equilibrium. The identification of redox-sensitive nutrient sensors in plant cells, their specificity, and the mechanisms involved in decoding these signals is one of the challenges for the field. A number of metal transporters, ion channels, MAPKs, and transcription factors appear to be regulated transcriptionally by ROS and NO, although the mechanisms underlying the network involved in this regulation is far from being completely known. Further methodological improvements will allow better identification of membrane proteins to more completely address the relevance of redox changes in the functionality of metal and ion transporters. However, the ·OH-dependent activation of Ca2+-permeable NSCC and SKOR has been well established. Here, we have also implemented our knowledge of NO-dependent regulation of metal transporters by the identification of cysteine and tyrosine targets of NO by an in silico analysis, which requires confirmation by further proteomic approaches. Interestingly, some transporters are targets of both ROS- and NO-dependent PTMs, suggesting that these reactive species are an important hub in the regulation of their functionality. Additionally, the specific subcellular sites of production of ROS and NO could trigger specific patterns of PTM and signaling, which require further deep analysis. All this information could be of interest in designing new strategies to fortify crops, improving plant resilience against nutritional imbalance and salinity, and designing new phytoremediation methodologies based on redox biochemistry governed by ROS and NO.Plants have developed an evolutionary strategy to regulate their response to environmental changes, including nutritional imbalance and toxic heavy metals and salinity, through a site-specific ROS and NO footprint (JXB online.The following supplementary data are available at S-nitrosylation and Y nitration sites of Arabidopsis metal transporters.Table S1. Prediction of erad349_suppl_Supplementary_Tables_S1Click here for additional data file.


Borrelia burgdorferi – the etiological agent of Lyme disease. We identify complex gene arrangements and operons, untranslated regions and small RNAs. We predict intrinsic terminators and experimentally test examples of Rho-dependent transcription termination. Remarkably, 63% of RNA 3′ ends map upstream of or internal to open reading frames (ORFs), including genes involved in the unique infectious cycle of B. burgdorferi. We suggest these RNAs result from premature termination, processing and regulatory events such as cis-acting regulation. Furthermore, the polyamine spermidine globally influences the generation of truncated mRNAs. Collectively, our findings provide insights into transcription termination and uncover an abundance of potential RNA regulators in B. burgdorferi.Transcription termination is an essential and dynamic process that can tune gene expression in response to diverse molecular signals. Yet, the genomic positions, molecular mechanisms, and regulatory consequences of termination have only been studied thoroughly in model bacteria. Here, we use several RNA-seq approaches to map RNA ends for the transcriptome of the spirochete  Borrelia burgdorferi, providing insights into various modes of transcription termination and identifying potential RNA regulators in this pathogen.Transcription termination can tune bacterial gene expression in response to diverse signals. Here, the authors use several RNA-seq approaches to map RNA ends for the transcriptome of the spirochete  Escherichia coli and Bacillus subtilis, much less is known about these processes in other important organisms, such as bacterial pathogens. For example, the fundamental aspects of gene expression have not been studied extensively in the spirochete Borrelia (Borreliella) burgdorferi, the etiological agent of Lyme disease – the foremost vector-borne illness in the United States1. The bacterium exists in a complex enzootic cycle2 that requires acquisition and transmission of B. burgdorferi between Ixodes scapularis ticks and small vertebrates. The survival and infectivity of B. burgdorferi depends on recognition and adaptation to these disparate environments with different physical conditions, temperatures, nutrients, and immune responses. Few studies have examined mechanisms of B. burgdorferi gene expression4, particularly transcription termination.All steps in gene expression – transcription, RNA processing, translation, and protein turnover – determine the ability of a cell to survive. Thus, they are highly regulated in all domains of life. While these regulatory mechanisms have been studied extensively in multiple model systems, such as 5. Intrinsic termination requires transcription of a GC-rich RNA hairpin followed by a U-rich tract, inducing RNA polymerase (RNAP) pausing and subsequent dissociation of the transcription elongation complex. Rho-dependent termination requires loading of the hexameric Rho protein complex onto nascent, untranslated RNA at Rho utilization (Rut) sites, which are proposed to be C-rich, G-poor, and unstructured6. Rho promotes termination and dislodges RNA polymerase usually 100–200 nt downstream of the Rut site, resulting in RNA 3′ ends that are commonly processed by 3′-to-5′ exonucleases8. Rho is essential in some bacteria, such as in E. coli9, but not in other species, like B. subtilis10. Recent studies have challenged the general view of two distinct termination processes. For example, intrinsic and Rho-mediated termination has been observed simultaneously at the same site, which may indicate they can function cooperatively13. Protein factors, like the transcription-elongation factors NusA and NusG, may also play a role in stimulating intrinsic termination, as recently implicated in B. subtilis14.In bacteria, termination has generally been thought to occur by one of two mechanisms: intrinsic  or Rho-dependentB. burgdorferi are limited. Rho has been annotated based on homology with E. coli Rho15, but has not been studied experimentally. Additionally, few studies have sought to determine B. burgdorferi intrinsic termination sites. In silico analysis of total RNA-seq data predicted 201 intrinsic terminators16 and a termination hairpin was identified within the bmpAB transcript17. However, it remains unknown how the structures of B. burgdorferi intrinsic terminators compare to those of other bacteria, what other factors are involved, and whether there is any overlap between termination processes.Studies of intrinsic and Rho-dependent termination in cis-acting RNA regulators are associated with secondary structure changes and premature termination and/or RNA processing within 5′ untranslated regions (UTRs) of mRNAs18. The cis-acting RNA structures have been shown to change in response to temperature (RNA thermometers), translation of small upstream open reading frames (uORFs), or by binding trans-acting factors, such as metabolites (riboswitches), tRNAs, RNA-binding proteins, or small base-pairing RNAs (sRNAs)24. sRNAs typically base pair with another RNA to elicit a regulatory response and can be transcribed independently or processed from the 5′ or 3′ UTR of mRNA transcripts, tRNAs, or internal to coding sequences25. Therefore, sRNAs can be generated and regulated by premature termination of a longer RNA. In turn, sRNAs themselves have been shown to regulate the termination of RNA targets by modulating Rho accessibility26. B. burgdorferi RNA-mediated regulation has not been well characterized. Two putative base-pairing sRNAs have been suggested to have regulatory roles and/or impact infectivity28, but their direct base-pairing function has not been demonstrated. Further, no cis-acting RNAs have been described in B. burgdorferi.In addition to being essential for gene expression, termination can be exploited for gene regulation. Many types of B. burgdorferi RNA expression, both in culture and during infection, is not well understood. Previous studies examined B. burgdorferi gene expression in simulated murine and tick environments31. However, these studies utilized microarray analysis based on previously annotated open reading frames (ORFs) and did not capture regulatory features like intergenic transcripts or prematurely terminated transcripts. More recently, RNA-seq approaches using spirochetes grown in Barbour-Stoenner-Kelly (BSKII) culture medium32 have led to further refinement of gene annotations and discovery of potential regulators. One such study estimated RNA boundaries and detected 353 possible sRNAs across bacteriological growth16. Another predicted 1,005 putative sRNAs, some dependent on temperature33. These studies are limited, however, by their use of total RNA-seq and size-selection RNA-seq respectively, which do not allow for precise resolution of transcript ends.Regulation of 36. 5′RNA-seq was previously applied to B. burgdorferi and used to discover promoters active during growth in culture and murine infection37. Here, we combined the previous 5′RNA-seq data with total- and 3′RNA-seq analysis to systematically update gene annotations, investigate transcription termination, and identify novel regulatory features. We predicted intrinsic terminators using canonical models of RNA hairpin formation/nucleotide composition and experimentally mapped instances of Rho-dependent transcription termination. We documented hundreds of sRNAs and mapped numerous 3′ ends within mRNA 5′ UTRs and internal to coding sequences, indicating that B. burgdorferi may harbor numerous RNA regulators. Finally, we demonstrated that the polyamine spermidine impacts the levels of internal 3′ ends and the generation of truncated transcripts. We characterized an RNA fragment that is internal to the spermidine transporter potB and is present across the enzootic cycle, particularly in fed ticks. We demonstrated that high extracellular spermidine concentrations led to an accumulation of potB 5′ fragments with a concomitant decrease in levels of the full-length mRNA. Taken together, our findings illustrate the power of complementary sequencing approaches to map RNA boundaries, provide insights into fundamental biological processes, and identify new RNA regulators in bacterial pathogens.Multiple RNA-seq approaches have been developed to globally identify both RNA 5′ and 3′ endsB. burgdorferi38 overlooked elements such as mRNA boundaries, sRNA genes, and translation initiation from non-canonical start codons. B. burgdorferi transcription start sites (TSSs) and 5′ processed RNA ends were previously detected using 5′RNA-seq37. Here we sought to identify RNA 3′ ends and combine datasets to globally predict RNA boundaries and refine the B. burgdorferi transcriptome annotation.Initial computational predictions of open reading frames (ORFs) in B. burgdorferi B31 (WT) were grown in BSKII to mid-logarithmic phase at 35 °C and then temperature-shifted ( ~ 23 °C to 35 °C) to stationary phase (see Methods for details). The temperature shifting (TS) protocol mimics the tick (23 °C) and mammalian (37 °C) temperatures and effectively induces rpoS40, the global stress-induced alternative sigma factor active during mammalian infection. Total RNA was isolated from the logarithmic (log) and TS-stationary phase cultures and analyzed using modified RNAtag-seq 41 and 3′RNA-seq 18  for the exponentially growing cells.We curated a list of statistically significant RNA 3′ ends  operon, which encodes the outer surface protein (OspA) required for B. burgdorferi tick colonization42. The combination of 5′RNA-seq37, total RNA-seq, and 3′RNA-seq allowed us to identify a TSS and multiple 3′ ends throughout the ospA/B operon  was also detected. Additional northern analysis with oligonucleotide probes upstream of two other 3′ ends in this operon revealed a novel 5′ derived sRNA and a previously-predicted ORF-internal sRNA (SR0884)33  also had this 3′ end and total RNA-seq reads suggested that pncA has a 290 nt 5′ UTR from which SR0754 is derived. Northern analysis with a probe directly upstream of the identified 3′ end (blue asterisk) confirmed co-transcription of SR0754 and pncA. Collectively, these data revealed complex transcriptional units, long UTRs, and multiple 3′ ends. These examples also illustrate how 3′ ends could be reflective of regulatory events and/or be associated with the generation of regulatory sRNAs.We additionally noted genes that harbored long 5′ UTRs with premature 3′ ends (before the end of the coding sequence). The nce Fig. . A previB. burgdorferi studies predicted either 1005 sRNAs by size-selection RNA-seq33, or 353 sRNAs by total RNA-seq16. We surveyed the 5′ and 3′ ends for these sRNAs in our dataset for logarithmically-grown B. burgdorferi (see Methods for details). This resulted in a list of 317 detectable sRNAs   Fig. S. bb0404 98 Fig. S, making 98 Fig. S and S3D.E. coli12 and Bacillus subtilis18. The B. burgdorferi TS-stationary 3′ ends had a similar distribution  according to their locations relative to annotated genes Fig. S. 3′ endson Fig. S. A subseon Fig. S.trans-encoded, base-pairing RNA regulators25.3′ UTR length was calculated for mRNA primary 3′ ends 152 total) detected for logarithmic-grown cells using the annotated stop codon  of intrinsic termination – RNA hairpin formation and a poly-U-tract causing transcriptional pausing to ultimately trigger termination. Sequences 50 nt upstream and 10 nt downstream of each 3′ end were analyzed and given an “intrinsic termination score”  overlap . 38.7% and 60.1% of our predicted intrinsic terminators were computationally predicted by this software using windows of 10 or 100 nt, respectively  has been performed on a variety of bacterial species, including gram-negative E. coli . Average terminator secondary structures for each organism were similar to the average structure for B. burgdorferi. The mean stem for B. subtilis was the longest at ~9 nt and B. burgdorferi had the longest average loop at ~11 nt. B. subtilis terminators also harbored the longest stretch of poly-A and -U. MEME analysis of sequences surrounding the 3′ ends with significant intrinsic terminator scores showed the expected poly-U enrichment for each bacterium. The P. aeruginosa termination logo also contained a conserved C/G rich stem sequence, and the B. subtilis logo, which was found in 90.9% of predicted terminators, had the strongest conserved A-track. Collectively, these data indicated general features of intrinsic termination are present across all bacteria examined. However, there were species-specific differences, with fewer B. burgdorferi 3′ ends preceded by canonical intrinsic terminators.Across oli Fig. , P. aeruosa Fig. , and B. lis Fig. , G/C usa6. Rho has been annotated in B. burgdorferi15, but no Rho-terminated genes have been reported. To experimentally map possible instances of Rho-dependent termination we treated cells with bicyclomycin (BCM), the selective inhibitor of Rho. Two independent B. burgdorferi cultures grown to logarithmic phase were split, with half left untreated and the other half treated with 100 µg/ml BCM for 6 h prior to RNA isolation. This concentration of BCM had marginal effects to B. burgdorferi growth in 6 h, but ultimately resulted in cell death . This ratio reflects the degree of readthrough when Rho-activity is inhibited, where a score of >2.0 is indicative of at least a twofold increase in readthrough in the +BCM compared to the -BCM sample. We found 393 genes to have regions putatively associated with a Rho termination event – downstream of 265 annotated ORFs, 26 tRNAs, and 102 sRNAs in both biological replicates  sitesth Fig. S. Total Rth Fig. S. Using t54. In B. burgdorferi, where rho is co-transcribed in a multi-gene operon, there was significant readthrough of rho upon BCM treatment and a Rho termination region was detected downstream of the rho stop codon  within its 5′ UTR with a Rho termination region within the bbk15 ORF . For example, SR0742, an annotated ~54 nt sRNA on replicon lp21, had significant transcriptional readthrough with a predominant longer form of the sRNA in the +BCM sample Fig. . These oE. coli Rho regions identified by ±BCM-seq12.Given that Rut sites are typically C-rich and G-poor, we calculated C:G nucleotide usage for all significantly predicted Rho termination regions, as well as for a control group of randomly selected genomic coordinates. Relative to the control group, there was a higher local C:G ratio within ~200 nt of the 3′ ends associated with Rho termination regions Fig. , similarE. coli for 31.2% of Rho-regions12. Recently, some studies have challenged the notion that Rho-mediated and intrinsic termination are mutually exclusive, suggesting cooperation at some terminators13. We selected a few genes with significant Rho and intrinsic termination scores for northern analysis. The gene manA, encoding the enzyme mannose isomerase38, had a Rho termination region detected downstream of the stop codon and a primary 3′ end with a high (12.5) intrinsic termination score  intrinsic termination score  Rho-termination events that overlapped with predicted intrinsic terminations (searching within and 800 bp downstream of annotated genes), which is nearly half (45.2%) of all predicted intrinsic termination associated with mRNAs Fig. . This waore Fig. . The ospore Fig. . The levE. coli could be the result of varied regulation12 including the binding of small base-pairing RNAs (sRNAs), the generation of 5′ derived sRNAs, translation of an upstream open reading frame (uORF), or conformational changes in cis-acting RNA elements (like RNA thermometers and riboswitches). Thus, we collated upstream and ORF-internal 3′ ends that could correspond to such regulatory events for B. burgdorferi (see Methods for details), resulting in a list of 870 3′ ends . Northern analysis across growth showed a dominant band corresponding to the expected SR0821 size ( ~ 82 nt) whose levels clearly increased in the temperature-shifted sample  binding adhesin and 5′ methylthioadenosine/S-adenosyl homocysteine (MTA/SAH) nucleosidase61. Bgp is predicted to be important for facilitating spirochete adhesion to the host extracellular matrix and for eliminating MTA/SAH, toxic byproducts resulting from adenine/methionine salvaging. We detected a novel short 5′ RNA ( ~ 58 nt) that corresponded to the 5′- and 3′RNA-seq signals  of ~333 nt that may correspond to the entire bgp 5′ UTR (195 nt), although the boundaries for this longer sRNA are not clearly defined in our RNA-seq data. The levels of these shorter 5′ RNAs decreased across growth with a concomitant increase in the levels of a transcript encoding bgp and the downstream pta gene. The difference in the ratio of the 5′ fragments to the full-length bgp RNA could be indicative of regulation.ls Figs.  and S6B.bb0401 . For the 5′ bb0401 RNA, a predominant band at the expected size of SR0292 ( ~ 202 nt) was observed  was previously reported to harbor a long ( ~ 195 nt) 5′ UTR that resulted in a premature transcript of the same length37. Here, we again detected 5′ derived fragments across growth  locus encodes an essential spermidine importer64. Specifically, the genes are predicted to encode an ATP-binding protein , two transmembrane proteins  and a polyamine periplasmic-binding protein 65. We observed possible premature transcription termination of the potB mRNA at a 3′ end that mapped 148 nt into the annotated potB ORF  documented three predominant products , and two shorter, likely 5′ processed transcripts, at ~ 228 nt (potB 5′334-561) and ~ 77 nt   Fig. S. There wcis-acting regulators, serve an independent function, and/or exist as the byproducts of regulatory events. These events may be influenced by environmental conditions, for example when the levels of a specific metabolite, such as nucleotides, nitrogen, peptides, carbon sources, or polyamines, are changed. To investigate this hypothesis, we exposed B. burgdorferi to spermidine and globally examined transcriptomic changes for 5′ and ORF-internal 3′ ends.In general, the short transcripts that we detected could correspond to cis-acting gene regulation in both eukaryotes and prokaryotes67. Three independent B. burgdorferi cultures were grown to stationary phase with or without 10 mM spermidine, after which total RNA was isolated and analyzed by RNA-seq. The replicate ±spermidine RNA-seq datasets were highly correlated  or as separate RNAs of approximately the same size (potA/B)  encodes a cytidine deaminase38. We observed a 3′ end that mapped 259 nt into the annotated bb0213 ORF  or the same sequence with potB 5′1-561 + 33 nt downstream of the premature 3′ end , which fuses the first 61 amino acids of PotB in-frame with luciferase  in luciferase activity was observed with the translational fusion exposed to 10 mM spermidine. These reporter-fusion data corroborated results from our northern analysis and suggest that potB expression is regulated at both the promoter and after transcription initiation.To investigate if the downregulation of se Fig. S. We assase Fig. S. Luciferse Fig. S. potB prpotB 5′1-561 effect could still occur with another promoter. Thus, we used the same translational luciferase reporter fusion but replaced the potB promoter with the potA promoter by incorporating 200 bp upstream of the potA TSS . In the 5′RNA-seq data, the number of TSS reads for PpotA and PpotB are similar , along with a vector control and a potA transcriptional reporter fusion control  under the same experimental conditions as before. As for the potB promoter, potA promoter activity decreased with the addition of spermidine, with only a marginal difference between 5 and 10 mM spermidine  RNA sequence harbors a predicted hairpin secondary structure and a series of rare codons, one being a proline, immediately upstream the ORF-internal 3′ end -qPCR using primers within SR0541 to measure the levels of potB 5′ RNAs across the tick-mouse enzootic cycle .We took an RNA-seq approach to globally map RNA 3′ boundaries in rpoS71. We observed differences in the levels of multiple transcripts when comparing late stationary samples to TS-stationary samples  stationary phase samples. While no growth condition in BSKII culture medium fully mimics environmental conditions, this approach has been utilized to robustly induce les Fig. . This suB. burgdorferi and revealed complex and overlapping transcription indicative of nuanced regulation. We updated previous gene annotations using the 5′- and 3′RNA-seq data. We note that this list . In a few cases, 3′ ends were absent from the RNA-seq datasets but observed by northern analysis. A lack of 5′ or 3′ end identification could be a result of sequencing depth or biases, such as reduced efficiency during RNA adaptor ligation or cDNA amplification. It is also possible a subset of RNA ends is missed due to RNA degradation during library preparation. Nevertheless, we think that our data and list of 3′ ends 17, which we also detected in concurrence with an ORF-internal sRNA (SR0274). Recently, a high percentage of ORF-internal 3′ ends also was reported for Mycobacterium tuberculosis77. The presence of ORF-internal RNAs may reflect regulated transcription termination or RNA processing which could alter transcript sizes based on growth stage or the environment of the bacterium, changing expression of the encoded gene products.Our data also suggest underappreciated regulation at the level of the transcriptome. The majority of our identified 3′ ends were located within annotated genes (59.1%), reminiscent of the previous observation that 63.0% of TSSs mapped internal to annotated 80 are needed to further refine protein-coding annotations.Moving forward, our list of gene annotations  and B. burgdorferi . Both organisms had a similar putative intrinsic structures and termination logos. It is somewhat surprising that there is not a stronger enrichment of G/C content surrounding B. burgdorferi 3′ ends, but given the A/T rich genome, perhaps limited G/C content in terminators is sufficient for the formation of stable secondary structures. In contrast, P. aeruginosa, which had the highest genomic G/C content of the organisms tested, had a strong enrichment of G/C content at 3′ ends. Of note, our classification of intrinsic termination is based on a previously described quantitative model of known E. coli and synthetic intrinsic terminators48. There could be novel features of B. burgdorferi terminators that prevent their “intrinsic” classification. Most instances of B. subtilis intrinsic termination require the protein factors NusA and/or NusG to facilitate efficient transcription termination14. It is worth testing if the B. subtilis model of protein factor-mediated intrinsic termination is applicable to bacteria where significantly fewer canonical intrinsic terminators are predicted. Genes encoding NusA and NusG are both annotated in B. burgdorferi38, but have not been studied, although NusA was found to co-purify with B. burgdorferi RNA polymerase81. Possibly, yet-uncharacterized termination factors are also involved in B. burgdorferi intrinsic termination. Further work, including in vitro assays using purified B. burgdorferi RNA polymerase, is required to define the “rules” of intrinsic termination in this pathogen.Transcription termination has been well-studied in model bacteria like B. burgdorferi termination using bicyclomycin treatment and mapped “Rho regions” by RNA-seq, the first report of Rho-dependent transcripts in B. burgdorferi. We observed 623 likely instances of Rho-mediated termination downstream of and within annotated ORFs, higher than the number of predicted intrinsic terminators. There is some controversy about when Rho associates with RNA and the RNA polymerase elongating complex, and the function/significance of C-rich Rut sites84. Our analysis suggested a limited increase in local C: G content preceding Rho termination regions in B. burgdorferi. We observed that some Rho regions were sharply defined by a dominant 3′ end, while others were stuttered with multiple 3′ ends. A well-defined 3′ end does not necessarily mean it arose from intrinsic termination, as even Rho-dependent 3′ ends may be preceded by regions of structured RNA or a termination event could be robustly 3′ processed to a specific nucleotide. We observed that 45.2% of predicted mRNA intrinsic terminators have downstream Rho regions. An overlap between intrinsic and Rho-mediated termination was also observed in E. coli12 and B. subtilis13. We carried out northern analysis on two B. burgdorferi examples, manA, and ospD, both with significant intrinsic terminator scores and a downstream Rho region. In both cases, we observed an overall decrease in the full-length mRNAs and an increase in shorter mRNA-fragments, when Rho was inhibited. This would suggest that both Rho and 3′ RNA secondary structures are required for appropriate termination and stability of the full-length transcript. Shorter RNA fragments may be generated as a byproduct of RNA turnover when termination occurs. It is also possible that this could serve as a regulatory mechanism, where the abundance or ability of Rho to bind could be titrated to control the length of an RNA produced under a specific condition.We documented that Rho is involved in B. burgdorferi86. The spirochete is predicted to encode endoribonucleases orthologs of RNase Y (bb0504), RNase III (bb0750), RNase HII (bb0046), RNase P (bb0441), RNase Z , RNase M5 , YbeY (bb0060) and exoribonuclease orthologs of Oligoribonuclease (bb0619) and PNPase (bb0805)87. Processed RNA ends often result in/depend on RNA secondary structures to stabilize the transcript. For example, the 3′ to 5′ exoribonuclease PNPase catalyzes nonspecific removal of 3′ ribonucleotides but is blocked by dsRNA structures formed at 3′ ends51. In some cases, dsRNA structures with low energies of dissociation may not uniformly protect the transcript, resulting in the clusters of minor 3′ ends that we observe. Transcription termination and the stability of mRNA 3′ ends are inherently variable processes impacted by multiple factors that can change across growth. Understanding the basic biological process of transcription termination and the contributing factors is important for understanding these regulatory processes.Many other detected 3′ ends in our dataset were not classified as either intrinsic or Rho-dependent terminators and could be generated by novel mechanisms of termination or result from RNA processing. Limited studies have examined RNA processing and turnover in 19, but these approaches may miss regulatory RNA elements, especially in organisms like B. burgdorferi where the functions of many genes are unknown and there is limited conservation compared to well-studied bacteria. Given that 3′RNA-seq is a sensitive, relatively unbiased, and genome-wide approach, we think it can provide evidence for RNA-mediated regulation for further study. Several long UTR sequences, such as pncA, bgp, and glpF, were found to harbor 3′ ends and stable small RNA fragments. In other cases, 3′ ends and sRNAs were detected within the start of a coding sequence as for bb0401 and potB. We hypothesize that a subset of these instances may reflect the presence of cis-acting regulation such as RNA thermometers and riboswitches. Bioinformatic predictions of riboswitches have suggested their presence in Spirochaetes88, yet the sole riboswitch characterized to date is a thiamine pyrophosphate (TPP) riboswitch in Treponema denticola89. We expect there are unique regulatory mechanisms for the numerous 3′ ends we detected.We focused on the abundant instances of 3′ ends in 5′ UTRs and ORFs, as we propose that their identification is an effective approach to discovering novel regulatory elements. Classically, mRNA internal regulators, such as riboswitches and attenuators, have been identified by serendipity, studies of individual genes, or searches for conserved RNA structures90. The global impact of spermidine in bacteria is less well studied. There are several examples in both bacteria and eukaryotes where translation of an upstream open reading frame (uORF) controls the expression of a downstream polyamine biosynthetic or regulatory gene. In E. coli, spermidine induces ribosome stalling at a uORF in the 5′ leader of the speFL mRNA92 and affects the translation of a uORF upstream the E. coli mdtJI mRNA12, encoding a spermidine exporter. In eukaryotic examples, when polyamine levels are high, there is enhanced ribosome occupancy on the uORF resulting in lower expression of the downstream gene95.To explore the utility of our approach to finding novel regulation, we investigated the effect of spermidine on transcripts detected. The polyamine spermidine globally affects translational fidelity by post-translationally modifying the eIF5A translation factor in eukaryotes, particularly for the translation of poly-proline residuesB. burgdorferi potB that increased in response to spermidine alongside downregulation of the full-length mRNA. The exact mechanism for this effect deserves additional study. However, given the overlap with the potA and potB coding sequence and the examples in other organisms, we hypothesize that potB 5′ RNAs could reflect the modulation of translation efficiency coupled with premature termination or mRNA cleavage. Our transcriptome-wide mapping of B. burgdorferi 3′ ends in response to spermidine also documented other examples of increased 5′ fragments at the start of coding sequences.We discovered a prematurely generated fragment of B. burgdorferi, spirochete gene regulation, and thus the biology of Lyme disease. We documented an abundance of potential cis- and trans-acting regulatory elements that set the stage for future projects to characterize RNA-mediated gene regulation in B. burgdorferi.Our work has led to new insights into the fundamental process of termination of transcription in B. burgdorferi strain B31 A3 lacking cp996, Adams lab strain number PA003, were used for all experimental studies. All strains, plasmids, and oligonucleotides used are listed in Supplementary Data Derivatives of infectious wild-type E. coli, grown in LB broth or on LB agar plates containing 300 μg/ml spectinomycin, prior to transformation into B. burgdorferi A3-68Δbbe02, which lacks cp9, lp56 and gene bbe02 on lp2597. The transcriptional luciferase reporter fusion plasmids were constructed by PCR-amplifying 200 bp upstream the potB or potA TSS37 using B. burgdorferi B31 (PA003) genomic DNA and the primers PA553 + PA554 or PA535 + PA536, respectively. The promoter sequences were ligated into PCR-amplified (PA480 + PA481) pJSB161 vector98 using the NEBuilder HiFi DNA assembly master mix, according to the manufacturer’s instructions (NEB). The potB translational luciferase reporter fusion plasmid was constructed by PCR-amplifying (primers PA400 + PA402) 200 bp upstream the potB TSS along with potB 5′1-561 + 33 nt downstream of the dominant potB ORF-internal 3′ end (PpotB+potB 5′1-594), which fuses the first 61 PotB amino acids in-frame with luciferase, and cloned into the pJSB161 vector cut with BlgII and BanII restriction enzymes. The codon-optimized potB translational luciferase reporter sequence was synthesized (GenScript) and then PCR-amplified (PA402 + PA531), prior to ligation into PCR-amplified (PA563 + PA590) pJSB161 vector using the NEBuilder HiFi DNA assembly master mix. The PpotA+potB 5′1-594 translational luciferase reporter fusion plasmid was constructed by ligating PCR-amplified products for PpotA (primers PA601 + PA602) and potB 5′1-594 (primers PA603 + PA594), which harbored an overlapping sequence into PCR-amplified (PA563 + PA590) pJSB161 vector using the NEBuilder HiFi DNA assembly master mix.The luciferase reporter plasmids were generated in NEB 5α B. burgdorferi were cultivated in liquid Barbour-Stoenner-Kelly (BSK) II medium supplemented with gelatin and 6% rabbit serum32 and plated in solid BSK medium as previously described99. B. burgdorferi cultures were grown at 35 °C with 2.5% CO2. Spirochete starter cultures were grown from a freezer stock, cell density enumerated by darkfield microscopy, and subcultured once by diluting spirochetes to 1 × 105 cells/ml for all experiments. Strains harboring luciferase reporter plasmids were grown in the presence of 50 μg/ml streptomycin. Temperature-shifted stationary cultures were grown to mid-logarithmic phase (3.0 × 107 – 5.0 × 107 cells/ml) at 35 °C, temperature shifted to the bench ( ~ 23 °C) for 48 h, and then temperature shifted back to 35 °C for 48 h resulting in a stationary culture (2.0 × 108 – 3.0 × 108 cells/ml). Growth curves were performed by diluting spirochetes to 1 × 105 cells/ml and monitoring cell growth at the indicated time points by darkfield microscopy enumeration.B. burgdorferi culture was collected by centrifugation, washed once with 1X PBS , and pellets snap frozen in liquid N2. RNA was isolated using TRIzol (Thermo Fisher Scientific) exactly as described previously100. RNA was resuspended in DEPC H2O and quantified using a NanoDrop (Thermo Fisher Scientific).45 ml of a B. burgdorferi B31 (PA003) were grown in BSKII to mid-logarithmic phase (3.75 × 107 – 5.5 × 107 cells/ml) at 35 °C, the culture split for RNA isolation or temperature shifted (TS) to bench ( ~ 23 °C), and then back to 35 °C and grown to stationary phase (~ 3.2 × 108 cells/ml). RNA was extracted as described above, resulting in logarithmic and TS-stationary samples. RNA was analyzed using an Agilent 4200 TapeStation System to check the quality. RNA was prepared for sequencing as described previously12. Any contaminating DNA in the samples was removed by treating 15 µg of RNA with 10 U of DNase I (Roche) for 15 min at 37 °C in the presence of 80 U of recombinant RNase inhibitor (Takara Bio). Next, RNA was purified by mixing the sample with an equal volume of phenol stabilized:chloroform:isoamyl alcohol (25:24:1) and centrifugation at maximum speed in Heavy Phase Lock Gel tubes (5 PRIME). A volume of chloroform equal to the original sample volume was added to the same Heavy Phase Lock Gel tubes and spun again. The aqueous layer was removed and ethanol precipitated. RNA pellets were reconstituted in 10 µl DEPC H2O and analyzed using an Agilent 4200 TapeStation System to ensure DNase-treated RNA was of high quality. 3′RNA-seq libraries were prepared using a modified version of the RNAtag-seq methodology41 based on the previously published Term-seq methodology18. 1.5 µg of DNA-free RNA was first ligated at the 3′ ends with 150 µM barcoded oligonucleotide adapters, which were 5′ phosphorylated and dideoxycytidine 3′ terminated  for 2 min at 72 °C, followed by addition of 1X Stop Solution (Invitrogen). Samples were stored on ice following individual fragmentation of each sample. Fragmented-RNA was pooled together and cleaned using the RNA Clean and Concentrator-5 kit (Zymo) according to the manufacturer’s instructions. Library construction continued following the bacterial-sRNA adapted, RNAtag-seq methodology starting at the rRNA removal step101. 3′RNA-seq libraries were analyzed on a Qubit 3 Fluorometer (Thermo Fisher Scientific) and an Agilent 4200 TapeStation System prior to paired-end sequencing using the HiSeq 2500 system (Illumina).Three biological replicates of 12. Raw sequence reads were trimmed with cutadapt 2.3102 to remove any adapters while performing light quality trimming with parameters “-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA -q 20 –minimum-length=25.” Sequencing library quality was assessed with fastqc v0.11.8 with default parameters and multiqc v1.10. The presence of common sequencing contaminants was evaluated with fastq_screen v0.13.0 with parameters “–subset 100000 –aligner bowtie2.” Trimmed reads were mapped to the B. burgdorferi reference genome (GCF_000008685.2_ASM868v2) using BWA-MEM. Multimapping reads were filtered using samtools103. Uniquely aligned reads were then mapped using subread featureCounts v2.0.1 with default parameters to gene features made from the union of NCBI B. burgdorferi B31 gene annotations GCF_000008685.2 and the updated sRNA annotation described below. BedGraph files were generated using deepTools104 on reads from each strand separately.Raw sequence reads were processed using lcdb-wf (lcdb.github.io/lcdb-wf/) according to the following steps as previously performed105 into a set of peaks that were reproducible across replicates. This code can be found at https://github.com/NICHD-BSPC/termseq-peaks and can be used in general for 3′RNA-seq (Term-seq) peak-calling in other bacteria. Termination peaks were subsequently curated according to the following criteria. The single-bp peak coordinate was set to the strongest signal nucleotide within the boundary of the initial broader peak using multiBigWigSummary from deepTools 3.2.1. The most downstream position, relative to the peak orientation, was chosen when several positions were equally strong. Scores from peaks within a distance of up to 100 bp were assessed to select the peak with the highest score among the cluster for further analysis. These curated peaks were used for all analysis herein , a prominence of , and a relative height of 0.75. Peaks for each replicate were then combined using the IDR framework, None, a106, ran via pybedtools v0.8.0107 and divides the peaks into: Primary , Antisense , Internal  and Orphan .Each peak was assigned to one or more classes, based on the intersect function of Bedtools 2.27.137 obtained using the same growth condition and B. burgdorferi strain as 3′RNA-seq was considered. All these 3′ ends were examined for the first upstream feature (either a TSS or an ORF stop codon). Any 3′ end where the first upstream feature was a stop codon was eliminated, unless there was also a TSS ≤200 bp upstream the 3′ end. Any 3′ end where a TSS was only 20 bp or less upstream was also eliminated. This resulted in the 3′ end coordinates in Supplementary Data 3′ ends were also categorized according to their position relative to mRNA 5′ UTRs and internal mRNA regions , 5′ processed end(s), and the most abundant 5′ and 3′ end for each sRNA . All refined sRNA annotations ; as of 2020-12-18. sRNA annotations (Supplementary Data ssrA/tmRNA (SR0030), ssrS (6 S) (SR0136)108, rnpB (SR0613), BsrW (SR0412)16, and IttA (SR0736)28. The following sRNAs were missed from Supplementary Data srp, ffs (4.5 S) (SR0360), DsrA (SR0440)27 and SR0726 109. The ORF annotation for pncA (bbe22) was updated to include the experimentally determined start codon110. 5′ UTRs were predicted by the presence of a TSS, with the highest average TAP+ sequencing read37, within a window of 300 nt upstream of an annotated start codon. 3′ UTRs were predicted by the presence of a primary 3′ end 100 nt downstream of an annotated stop codon. UniProt gene names and descriptions from the UniProt B. burgdorferi B31 database  were imported to supplement the gene information reported from NCBI. Reference to gene bb0268 as ‘hfq’ was removed, as this gene was previously misannotated. Collectively, these data resulted in Supplementary Data 48 to score putative intrinsic terminators, which uses KineFold to simulate the kinetic folding of mRNA as it is transcribed by RNAP111. The results for each 3′ end region are included in Supplementary Data 112 package 0.11.). The average GC per position is given below the heatmap. Terminator logos were generated using MEME113 5.4. with the RNA-specific default.Each identified 3′ end and its surrounding sequence  was analyzed using a previously described quantitative modelB. burgdorferi 3′ ends identified by 3′RNA-seq with 3′ ends and predicted terminators identified in other studies. Specifically, we determined how many of the positions in our list of genome coordinates are within a given distance threshold of a genome coordinate from another study.As detailed in Fig. SB. burgdorferi termination to other bacteria, we analyzed raw 3′RNA-seq (Term-seq) data from E. coli , P. aeruginosa  and B. subtilis  were downloaded with sra-toolkit fastq-dump v2.9.1_1, and processed according to our established “Identification of 3′ ends from Term-seq” pipeline. The published E. coli data (previous work from our groups12) was analyzed using lcdb-wf v1.7. To keep the analysis consistent between all organisms and avoid version discrepancies, the raw E. coli sequencing data was re-analyzed using lcdb-wf v1.7, specifically using the same versions of all software dependencies to ensure consistency across analyses. G/C content and putative terminator logos for other bacteria were analyzed the same as B. burgdorferi 3′ end sequences.To compare features of 41, which was adapted to capture bacterial sRNAs101. Total RNA-seq RNA libraries were sequenced as for 3′RNA-seq. Total RNA-seq data processing followed the same procedures as 3′RNA-seq data analysis for QC, adaptor removal and sequencing read mapping.Total RNA-seq was performed using the same RNA that was used for the 3′RNA-seq library preparations. Total RNA-seq library construction was carried out based on the RNAtag-seq methodologyB. burgdorferi B31 (PA003) spirochetes were grown in BSKII to a cell density of 4.0 × 107 (replicate #1) or 2.75 × 107 (replicate #2) cells/ml (logarithmic phase), the cultures divided and half treated with 100 μg/ml of bicyclomycin benzoate  for 6 h. Total RNA was isolated from the untreated and BCM-treated cultures as described above. The RNA libraries were prepared and processed according to the total RNA-seq procedure, using the RNAtag-seq methodology as described above.Two cultures of 12 (available at https://github.com/gbaniulyte/rhoterm-peaks). The ratio of the read count in a 800 nt region upstream (BCM_us) and downstream (BCM_ds) of each position was compared to the same ratios at the same position in the untreated sample  (R(BCM/untreated) = (BCM/Untreated) threshold for termination sites was set to ≥2.0. Only the position with the highest R(BCM/Untreated) value within an 800 nt window upstream and downstream was reported in Supplementary Data P-values were calculated by a two-sided Fisher exact test. An ‘undefined’ Rho score indicates one that could not be calculated due to an absence of reads in one or more ±BCM 800 nt upstream or downstream regions. A significance score of ‘n/a’ indicates that the Rho significance score was too low (<1e−300) to accurately report and indicates a highly significant score. No significance scores were calculated for undefined scores. The presence of a Rho region was also indicated for each gene when one was identified on the same strand internal to or 800 bp downstream of an mRNA ORF, tRNA or sRNA, in either or both RNA-seq replicates  spirochetes were diluted into BSK-II or BSK-II + 10 mM spermidine  adjusted to pH 7.8. Cells were collected in stationary phase ( ~ 1×108 – 2×108 cells/ml) after ~80 h of growth in BSK-II alone or ~96 h of growth in BSK-II + 10 mM spermidine. Total RNA was isolated from the untreated and spermidine-treated cultures as described above. The RNA libraries were prepared and processed according to the total RNA-seq procedure using the RNAtag-seq methodology as described above and sequenced using the Nova-seq system.Three cultures of B. burgdorferi; to estimate the expression of such overlapping genes, the quantification was performed two ways, one that included reads overlapping 2 or more features , and one performed with standard quantification parameters that included only reads overlapping a single feature. Differential expression was performed  was <0.1 (default for DESeq2) for the statistical test that the magnitude of the log2-fold change is greater than 0 .Sequencing reads from untreated and spermidine-treated samples were aligned to the GCF_000008685.2_ASM868v2 genome by following the same procedures as 3′RNA-seq data analysis for QC, adaptor removal and sequencing read mapping. Raw counts were quantified using featurecounts from the subread package v.2.0.1. Numerous gene features overlap other features in (untreated/spermidine) = (untreated/spermidine) threshold for spermidine dependency was set to ≥ 2.0. All upstream and ORF-internal 3′ ends were given a spermidine-dependent score (Supplementary Data (untreated/spermidine) value calculated across a 50 nt window upstream and downstream of each 3′ end. P-values were calculated by a two-sided Fisher exact test. An ‘undefined’ spermidine-dependent score indicates one that could not be calculated due to an absence of reads in one or more ±spermidine 50 nt upstream or downstream regions. A significance score of ‘n/a’ indicates that the spermidine-dependent significance score was too low (<1e−300) to accurately report and indicates a highly significant score. No significance scores were calculated for undefined scores.Processed sequence reads from “Differential expression in ±spermidine conditions” were used. Spermidine-dependent scores were calculated as Rho region scores, with the difference that the minus spermidine ratios were compared to the plus spermidine ratios  with 0.08% ammonium persulfate) and transferred to a Zeta-Probe GT membrane (Bio-Rad). For longer RNAs, 10 μg of RNA were fractionated on a 2% NuSieve 3:1 agarose (Lonza), 1X MOPS, 2% formaldehyde gel and transferred to a Zeta-Probe GT membrane (Bio-Rad) via capillary action overnight. For both types of blots, the RNA was crosslinked to the membranes by UV irradiation. RiboRuler High Range and Low Range RNA ladders (Thermo Fisher Scientific) were marked by UV-shadowing. Membranes were blocked in ULTRAhyb-Oligo Hybridization Buffer (Ambion) and hybridized with 5′ 32P-end labeled oligonucleotides probes  and transferred to a nitrocellulose membrane (Thermo Fisher Scientific). Membranes were blocked in 1X TBST containing 5% milk, probed with a 1:2,000 dilution of polyclonal α-PotA65 followed by a 1:10,000 dilution of peroxidase labeled α-mouse  and developed with SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific) on a Bio-Rad ChemiDoc MP Imaging System.Immunoblot analysis was performed as described previously5B. burgdorferi B31 A3 (PA003) spirochetes. The inoculum dose was verified by colony-forming unit (CFU) counts in solid BSKII medium. Three weeks post inoculation, infection was confirmed by serology, as previously described115. Approximately 200 uninfected Ixodes scapularis larvae (Oklahoma State University) were allowed to feed to repletion on the B. burgdorferi infected mice, as previously described116. Two weeks from the tick placement, groups of 25 fed larvae were pooled, snap frozen in liquid nitrogen, and processed for RNA isolation. The remaining fed larvae were maintained at 23 °C under 98% humidity and allowed to molt to nymphs. Four groups of 25 nymphs each were fed to repletion on individual naïve 6–8 week old female C3H/HeN mice. Two weeks from the tick placement, groups of 5 fed nymphs were pooled, snap frozen in liquid nitrogen, and processed for RNA isolation. For the isolation of spirochete RNA from mouse tissues, six 6–8 week old female C3H/HeN mice (Envigo) were needle inoculated intraperitoneally (80%) and subcutaneously (20%) with 1 × 105B. burgdorferi B31 A3 (PA003) spirochetes. Ten days post inoculation mice were sacrificed and groups of 2 mouse bladders were pooled, snap frozen in liquid nitrogen, and processed for RNA isolation. Infection was confirmed by the reisolation of spirochetes from ear, heart, and joint tissues.For the isolation of spirochete RNA from ticks, six 6-8 week old female C3H/HeN mice (Envigo) were needle inoculated intraperitoneally (80%) and subcutaneously (20%) with 1 × 10Eunice Kennedy Shriver National Institute of Child Health and Human Development Institutional Animal Care and Use Committee.The National Institutes of Health is accredited by the International Association for Assessment and Accreditation of Laboratory Animal Care. All animals were cared for in compliance with the Guide for the Care and Use of Laboratory Animals. Protocols for all animal experiments were prepared according to the guidelines of the National Institutes of Health, reviewed and approved by the B. burgdorferi B31 (PA003) were grown to mid-logarithmic phase (2.75 × 107 – 5.25 × 107 cells/ml), the cultures split for RNA isolation or allowed to grow an additional 48 h to stationary phase (2.18 × 108 – 3.08 × 108 cells/ml) in BSKII. Cells were pelleted at the indicated time points, washed once with 1X PBS, and snap frozen in liquid N2. Frozen culture-pellets, tick, or mouse bladder samples were homogenized in ~1 ml TE buffer  containing 0.5 mg/ml lysozyme in gentleMACS M Tubes using a gentleMACS Dissociator . RNA was isolated using a hot phenol/SDS protocol. Briefly, homogenized samples were mixed and incubated with 1% SDS, 0.1 M NaOAc, pH 5.2 and equal parts saturated phenol (pH 6.6) at 64 °C for 6 min. Samples were centrifuged for 10 min at 4 °C on maximal speed and the upper phase ( ~ 0.6 ml) was transferred into a Heavy Phase Lock Gel tube (5 PRIME) with 800 μl of buffer-saturated phenol:chloroform:isoamyl alcohol (25:24:1). Samples were mixed thoroughly by inversion and centrifuged for 7 min at 4 °C on maximal speed. The aqueous layer was transferred to a new Heavy Phase Lock Gel tube and 800 μl of chloroform was added. Samples were mixed and spun again. The aqueous layer was removed and ethanol precipitated. RNA pellets were reconstituted in DEPC H2O. For tick RNA samples, any contaminating DNA was removed by treating 1–5 μg of RNA with the TURBO DNase enzyme kit and purified according to the manufacturer’s instructions (ThermoFisher Scientific). For mouse bladder RNA samples, any contaminating DNA was removed by treating 25 μg of RNA with 10 U of DNase I (Roche) for 20 min and purified as previously described12.Three biological replicates of potB 5′), PA635 and PA636 (potB ORF 3′), PA495 and PA496 (ospC), and PA489 and PA490 (recA). Reactions were placed in CFX Opus 96 (Bio-Rad) with the following program specifications: Step 1: 95 °C 5 min; Step 2: 95 °C 10 sec, 55 °C 30 sec – repeat Step 2 for 40 cycles; Step 3: 95 °C 15 sec, 55 °C to 95 °C melt curve. The amounts of each transcript were determined using a genomic DNA standard curve for each gene target with 10 ng, 1 ng, 0.1 ng, and 0.01 ng of DNA. Both standard-curve reactions and PCRs using cDNA for each biological replicate were performed in technical duplicate or triplicate. mRNA transcript copy numbers were normalized to recA copies. See Source Data file for raw data.cDNA was synthesized from DNase-treated RNA from cell-culture, fed larvae, fed nymph and mouse bladder samples using the iScript Reverse Transcription supermix kit according to the manufacturer’s instructions (Bio-Rad). Parallel cDNA reactions were carried out in the absence of reverse transcriptase. Real-time quantitative PCR reactions were performed using SsoAdvanced universal SYBR green supermix and the following primer pairs: PA630 and PA634 (117. B. burgdorferi clones were subcultured to a density of 1 × 105 cells/ml in 15 ml of BSKII and after 48 hours of growth (1.0 × 107 – 3.5 × 107cells/ml), pelleted at 4000 rpm for 10 min. Cells were resuspended in 15 ml of BSKII containing either no spermidine, 5 mM spermidine or 10 mM spermidine . Prior to resuspension, media containing spermidine was pH adjusted to 7.8 to match BSKII not containing spermidine. After an additional ~40 h of growth (1.1 × 108–1.7 × 108 cells/ml), cells were pelleted at 4000 rpm for 10 minutes, washed with phosphate-buffered saline (PBS)  and resuspended in 225 μl of PBS. 100 μl of each sample was used to measure the optical density at 600 nm (OD600) using an EnVision 2105 Multimode Plate Reader (PerkinElmer). 93 μl of each sample were loaded into a black, solid bottom 96-well plate (Corning) and combined with 7 μl of 10 mM D-luciferin (Regis) in PBS. The relative luciferase units (RLUs) were determined by measuring photon emission in each well for 2 s using the EnVision 2105 Multimode Plate Reader, 5 min after the addition of luciferin. The background RLU was determined by measuring a PBS control, which was subtracted from all experimental measurements. Background-subtracted RLUs were normalized to the OD600 value of the same sample (RLU/OD600). All experiments were conducted in biological triplicate. B. burgdorferi harboring an empty vector (PA156) was used as a control for all experiments. See Source Data file for raw data and statistical analysis.Luciferase assays were modified fromFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Lentiviral vectors (LV) have proven to be powerful tools for stable gene delivery in both dividing and non-dividing cells. Approval of these LVs for use in clinical applications has been achieved by improvements in LV design. Critically important characteristics concerning quality control are LV titer quantification and the detection of impurities. However, increasing evidence concerning high variability in titration assays indicates poor harmonization of the methods undertaken to date. In this study, we developed a direct reverse transcription droplet digital PCR (Direct RT-ddPCR) approach without RNA extraction and purification for estimation of LV titer and RNA genome integrity. The RNA genome integrity was assessed by RT-ddPCR assays targeted to four distant regions of the LV genome. Results of the analyses showed that direct RT-ddPCR without RNA extraction and purification performs similarly to RT-ddPCR on purified RNA from 3 different LV samples, in terms of robustness and assay variance. Interestingly, these RNA titer results were comparable to physical titers by p24 antigen ELISA (enzyme-linked immunosorbent assay). Moreover, we confirmed the partial degradation or the incomplete RNA genomes in the prepared 3 LV samples. These results may partially explain the discrepancy of the LV particle titers to functional titers. This work not only demonstrates the feasibility of direct RT-ddPCR in determining LV titers, but also provides a method that can be easily adapted for RNA integrity assessment. Retroviridae family are spherical to pleomorphic enveloped particles, are approximately (80 to 100) nm in diameter, and their genome possesses two copies of a positive-sense single-stranded RNA inside a conical capsid2.Lentiviral vectors (LVs) belonging to the 3.LVs have emerged as powerful and versatile gene transfer vectors for ex vivo and in vivo gene therapy as they can stably integrate into the target cell genome, providing long-term transgene expression, and are capable of delivering a large genetic therapeutic cassette (up to 10 kb) into dividing and non-dividing cells6.To date, there are 201 gene therapy clinical trials using LVs, including 86 CAR-T cell trials that utilize LVs to transduce the T cells and 3 CAR-NK cell trials that utilize LVs to transduce the NK cells . Six LV- based CAR T-cell therapies  have been approved by the Food and Drug Administration (FDA). In efforts towards delivery of safe and efficacious doses of LV based gene therapy products, the World Health Organization, Lentigen, and researchers in France have independently developed standards and reference materials for vector copy number (VCN) and LV integration sites measurements7. To assess process efficiency in terms of yield and recovery, rapid and sensitive quantification methods for LV characterization are highly desired for analyzing crude supernatants and final purified products.Clinical LV preparations are typically produced by transient plasmid-mediated co-expression of viral components in the HEK293T cell line. The production of LVs is therefore a critical step in developing gene therapy as an efficient and cost-effective treatment15. However, there is high variability (100s to 1000s fold differences) in the obtained results, with most of these methods overestimating the functional titer. For instance, in the p24 ELISA, which is only applicable for HIV-1 derived LVs, the protein pool includes a variable amount of free p24 and p24 associated with non-functional vector particles16. Non-functional particles are also measured by the RNA titration method. The titer of functional LVs are cell-based methods that mostly done by FACS and PCR-based analysis. FACS analysis is limited to the vectors with expression of fluorescent transgenes and cannot discriminate cells with single or multiple integrations17. In addition, results from these cell-based assays are time consuming and complicated, rendering them unsuitable as a routine characterization method for in-process samples.There has been an increase in efforts to develop rapid and robust quantitative characterization methods to measure functional and non-functional lentiviral titer. Currently, many methods for functional and non-functional titer have been described, including HIV-1 p24 antigen enzyme-linked immunosorbent assay (ELISA), RNA vector genome titers, reverse transcriptase activity (RT), dot blot methods, fluorescence-activated cell sorting (FACS) flow cytometry, antibody-based detection on the Virus Counter 3100 , high-performance liquid chromatography (HPLC), and polymerase chain reaction (PCR)Rapid and robust quantitation methods for LVs are significant needs in gene therapy. In this study, we developed a direct reverse transcription droplet digital PCR (direct RT-ddPCR) approach without RNA extraction and purification for estimation of LV titer and RNA genome integrity. The RNA genome integrity was assessed by using RT-ddPCR assays against four distant regions of the LV RNA genome. We developed direct, one-step RT-ddPCR assays to reduce both sample handling time and variability. Moreover, we used a lentiviral RNA calibrator to calculate the reverse transcription efficiency of different targets on RNA genome from LV samples.LV samples were purchased from VectorBuilder  and Vigene . Another LV sample was kindly provided by GSK PLC . VectorBuilder LVs were designed at National Institute of Standards and Technology (NIST) and packaged at VectorBuilder. The viral particles were aliquoted and stored in Hanks Balanced Salt Solution  buffer at − 80 °C until use. The three LV samples were referred to as samples A, B, and C for analysis.20.A lentiviral p24 ELISA titration kit was purchased from Takara Bio Inc. USA . Total p24 was quantified by ELISA following the kit protocol. Free p24 was also quantified by ELISA without adding viral lysis buffer to the reactions. Briefly, 20 µL of viral lysis buffer was added to each well, except for the wells used for free p24 quantification; 200 μL of standard p24 serial dilutions and viral sample serial dilutions were added to each well. The plate was incubated at 37 °C for 1 h. The samples were removed from the wells and the wells were washed with ELISA washing buffer 3 times. A volume of 100 µL of biotin conjugated anti-p24 detector antibody was then added into each well and the plate was incubated at 37 °C for 1 h. The antibodies were aspirated, and the wells were washed with washing buffer 3×. One hundred microliters of Streptavidin-HRP conjugate were added into each well and incubated at room temperature for 30 min. The conjugate was then aspirated, and the wells were washed with washing buffer 3×. One hundred microliters of substrate solution were added into each well and incubated in the dark at room temperature for 30 min. One hundred microliters of stop solution were then added into each well and 450 nm absorption was measured by plate reader . Titer of the lentivirus was calculated based on the assumption of each lentiviral particle containing 2000 molecules of p246 copies of SARS-Cov 2 RNA  spike-in, and incubated at room temperature for 10 min. The lysate was then mixed with 560 µL of 100% ethanol. The ethanol and viral lysate mixture was loaded onto a QIAamp Mini column and centrifuged at 6000×g for 1 min. The column was then washed with 700 µL wash buffer AW1 once and twice with 500 µL wash buffer AW2. A final spin at 12,000×g, for 1 min was performed to remove the residual wash buffer, and the viral RNA was eluted with 50 µL of buffer AVE. The RNA samples were stored at -80 °C until use.Viral RNA isolation was performed using a Qiagen QIAamp viral RNA mini kit  by following the kit manufacturer’s protocol. Briefly, 50 µL of lentivirus particles was lysed with 560 µL of Buffer AVL in a microcentrifuge tube with/without 2 × 10The sizes of isolated viral RNA from lentivirus particles were analyzed on Agilent Bioanalyzer 2000 by using Agilent RNA 6000 Nano Kit  and following the instructions of the kit.8 copies based on the calculated molecular weight.A DNA template was generated by amplifying most of the lentiviral genome from the GSK lentivirus sample (produced from transduced HEK 293 cells at NIST), using the 5′ primer sequence located at the 5′ long terminal repeat (LTR), and the 3′ primer sequence located at the junction of the woodchuck hepatitis virus post-transcriptional regulatory element (WPRE) and 3′ LTR (forward primer: TAATACGACTCACTATAGGGGTCTCTCTGGTTAGATCT and reverse primer: TGCCTTGTAAGTCATTGGTCTTAAAG). A T7 promoter sequence was added to the 5′ primer sequence. As a result, a 3823 bp DNA fragment . Briefly, eluted RNA was serially diluted in RNA storage solution (RSS) until reaching the detection range of ddPCR, and then combined with reagents from the One-Step RT-ddPCR Kit and two primer/probe sets each with final primer concentrations of 900 nmol/L and probe concentrations of 50 nmol/L. To measure viral RNA genome copies, the ddPCR assay target 5′ LTROne-step direct RT-ddPCR experiments were performed to measure the lentiviral RNA genome copy numbers by using similar protocol as One-step RT-ddPCR without RNA extraction from the viral particles. Treated or untreated lentiviral particles instead of diluted RNA were directly applied to the RT-ddPCR reaction mixtures. Lentiviral particles were heat treated at different temperatures from (56 to 95) °C for (10 or 15) min Fig. . UntreatP values less than 0.05 was considered as significant between the samples compared.Statistical analysis was carried out by using GraphPad Prism version 9.4.1 software or Microsoft Excel. All data presented here is mean ± standard deviation. Certain commercial equipment, instruments, and materials are identified to specify the experimental procedure. In no case does such identification imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment are necessarily the best available for the purpose.9 ± 1.5 × 108, total p24: 1.4 × 1011 ± 1.1 × 1010). Calculated from the molecular weight of p24, 1 pg of p24 is equivalent to 2.5 × 107 molecules. Each lentivirus contains about 2000 p24 molecules, therefore, 1 pg of p24 is about 1.25 × 104 lentivirus particles. The equation for lentivirus titer calculation is: p24 amount (pg) × 1.25 × 104 × dilution factor.All lentivirus samples were titrated by p24 ELISA to confirm the physical titer. The lentivirus particles were diluted in PBS with 10% FBS by serial dilution. After determining the dilution range within the ELISA kit linear detection range, 3 serial dilutions of each sample were used for titration. All 3 dilutions resulted in similar p24 titer (data not shown). Interestingly, the p24 ELISA titration method from our experiments showed different titers compare with other titration methods  was used as a calibrator. After the lentivirus particles were lysed in the lysis buffer, 2 × 102 > 0.997. We then tested 5 sets of primers/probe for titrating lentiviral RNA genome in the direct RT-ddPCR reactions. The LV samples were serially diluted and used for direct RT-ddPCR without any treatment. All 5 sets of primers/probe detected lentiviral RNA genome without RNA extraction  to PBS will reduce lentivirus particles binding to the tube wall and increase the RT-ddPCR detection level. To test this hypothesis, we performed serial dilution of Sample C in PBS and PBS containing 10% FBS before heat inactivation and RT-ddPCR. As shown in Fig. Within the lentivirus particles, there could be empty particles without RNA genome, truncated RNA genome, or intact RNA genome. To measure the lentiviral RNA genome integrity, we first synthesized a GSK lentiviral RNA genome in vitro as calibrator. Dilution linearity and reverse transcription efficiency (RT efficiency) was analyzed in one-step RT-ddPCR and the results are shown in Fig. We utilized the synthesized lentiviral RNA (derived from the GSK sample) as a calibrator to measure the integrity of RNA that was packed inside the lentivirus particles. The detected copy numbers of each element from lentiviral RNA genome were normalized to its RT efficiency, and the element with the highest detection level was assigned as 100% Fig. . For mos11 ± 3.61 × 109 from one step direct RT-ddPCR and 1.35 × 1011 ± 1.30 × 108 from RNA extraction sample . It can reduce the RNA handling time and variability from the RNA extraction process.The increasing application of LVs in gene transfer and/or gene therapy demands a rapid and robust measurement of LV titers and quality. There are a few existing LV titration methods, such as p24 ELISA, RNA extraction followed by qPCR, and cell-based assays. P24 ELISA measures the amount of LV capsid protein p24 and assumes that each LV particle has 2000 molecules of p248/mL, whereas p24 titer was 1.9 × 1011, which was approximately 100-fold higher. By using 5 different ddPCR targets and the RNA calibrator, we detected that about 16.34% of the RNA genome in Sample A was intact. The ddPCR targeting LTR showed the lowest titer among the 5 PCR targets (4.31 × 109 ± 7.12 × 108) and it was close to the transduction titer we determined by using HEK293 cells (9.94 × 108 ± 9.0 × 106). Lentivirus transduction efficiency varies based on the transduction protocol, cell type, cell quality and other factors. Taken together, this one-step direct RT-ddPCR method provides a rapid and robust measurement of LV titers and quality.When the lentivirus particles are packaged within the host cells, the lentivirus particles can contain partial RNA genome, no RNA genome (empty virus particles), or an intact RNA genome. Most of currently used methods to titrate the LVs are not able to determine the integrity of the LV RNA genome. In this study, we developed a method to measure the integrity of LV RNA genome. By using an RNA calibrator, we were able to determine the RT efficiency of each ddPCR target element and determine the percentage of lentiviral particles that have intact RNA genomes Fig. . Capsid Supplementary Information.


While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to RNA-Seq such as allele-specific expression (ASE).Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples.https://gitlab.com/evigorito/bbmix and https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.We implemented BBmix as an R package that is available for free under a GPL-2 licence at  RNA-sequencing (RNA-Seq) is an established and popular technique with applications in transcript quantification and detection of alternative splicing, among others. More recently, a growing number of studies have developed and tested pipelines to use RNA-seq for variant and genotype calling (n reads covering a single nucleotide polymorhism (SNP), we would expect 0, n/2, and n to contain the alternative allele for genotypes 0, 1, and 2, respectively. Sampling variation and reference mapping bias may mean the observed heterozygote genotype count deviates from n/2 particularly when n is small, and the homozygote observations may deviate minorly from 0 and n due to sequencing or mapping errors. However, when n is large, the genotypes are expected to be clearly distinguishable. In RNA-seq, the heterozygote observation may show much greater variance, because heterozygosity at local regulatory sequences may cause overamplification of the allele on one chromosome over the allele on its homologue, with this effect varying between samples as genotypes at the regulatory sequences vary. This may push the observed heterozygote count nearer to 0 or n, making a miscall as a homozygote more likely, particularly when n is small.RNA-seq genotyping pipelines employ RNA-seq-based aligners and quality control steps for filtering out potentially erroneous calls, but use the same statistical models as for DNA sequencing. While adopting these methods provides a convenient way to call variants from RNA-seq, there are sources of variation in RNA-seq data that are not accounted for. In DNA sequencing, in an ideal world reads would follow a binomial model, then, out of https://www.nist.gov/programs-projects/genome-bottle), the subset of GBR samples from the Genetic European Variation in Disease (GEUVADIS) project , for which RNA-seq data and high-quality genotype calls are publicly available and samples from the Pathobiology of Early Arthritis Cohort (PEAC) cohort , a two-step method based on first modelling the genotype-specific read counts using beta-binomial distributions and then using these to infer genotype posterior probabilities. We benchmarked our method using high confidence calls from the Genome in a Bottle consortium , and we assume that the distribution of reads differs according to its value. Given fSNPs indexed by l, we denote alR as the number of reads overlapping its alternative allele and tlR the total number of reads mapping ls. We then fit a mixture of three beta-binomial distributions to the allelic counts of a random sample of L fSNPs to estimate the parameters  for the underlying distribution of genotypes. With λ a vector of the proportion of each component, we express the likelihood aswhere BB is the beta-binomial distribution:which we reparameterize to:We selected RNA-seq reads overlapping a predefined set of feature SNPs (fSNPs) that we aim to genotype. We treat genotype as a latent variable, We put the following priors:We selected the Dirichlet prior as the natural choice for a set of probabilities that sum to 1, and the informative priors for α and β were selected by visual inspection of the fit of beta distributions to the empirical distribution of reads for a collection of fSNPs with effect allele frequency (EAF) higher than 0.01 .n_eff and Rhat statistics as well as visual examination. We obtained 4000 posterior observations sampled from four chains for each parameter. For each chain, we discarded the first 1000 observations.We estimated μ and λ by Markov chain Monte Carlo implemented in Stan . We checWe called genotypes on a per-individual basis. Details can be found in We used RNA-seq data from lymphoblastic cells lines derived from NA12878 and 13 additional samples (study E-MTAB-1883) with the high confidence genotype calls from the Genome in a Bottle consortium for sample NA12878 used as a gold standard. Note that only non-reference homozygote genotypes are available in the vcf files for this data source. The Genome in a Bottle consortium also provides BED files with high confidence sequencing data for defined regions across the genome for sample NA12978. Although it would be safe to assume that any site included within these high-confidence regions for which variants have not been called are homozygous reference i we did not include those positions in our analysis. We also selected a subsample of 86 individuals of European ancestry coded as GBR from the GEUVADIS for which genotypes and RNA-seq data are publicly available and genotypes and RNA-seq data from the PEAC study. Details for data sources both for genotypes and RNA-seq can be found in https://gatk.broadinstitute.org/hc/en-us/articles/360035531192-RNAseq-short-variant-discovery-SNPs-Indels-) for read alignment and data cleanup  before calling variants. Details on the implementation are described in We applied the GATK pipeline for RNA-seq short variant discovery . Each method was run with default parameters. Details of the implementation are in We validated BBmix against three other popular methods: HaplotypeCaller , and Frehttps://gitlab.com/evigorito/bbmix and https://cran.r-project.org/package=bbmix. The pipeline for preparing the inputs for running bbmix can be found at https://gitlab.com/evigorito/bbmix_pipeline. The pipeline and code used for preparing the manuscript is at https://gitlab.com/evigorito/bbmixpaper_pipeline. The versions of all the software tools used for producing the bbmix R-package, pipeline, and manuscript are listed in The R package with the code for genotyping using bbmix can be found at 21 sets in total) were randomly selected from the Genome in a bottle sample NA12878. We fit the BBmix model independently on each subset. As expected, increasing the number of SNPs resulted in a narrower posterior distribution of the mean parameter of each component  model on the accuracy of genotype calls. To this end, three sets of 500, 1000, 2000, 3000, 4000, 5000, or 10 000 SNPs with at least 10 mapping reads  while the loss in the number of calls was 0.15%  using ASE, which requires genotyping of exonic SNPs  tends to be sufficient to learn the model parameters in less than an hour, and that genotyping was very consistent even when using an external sample, which completely eliminates the need for model training. Thus, our work presents a method tailored for genotyping using RNA-seq and also highlights that the current methods for variant-calling using DNA could be adapted for RNA-seq by modelling RNA-seq count biases.btad393_Supplementary_DataClick here for additional data file.


Supplementary Data.The functioning of the cell plasma membrane depends on its lipid and protein compositionsSupplementary Table S1). The spectra of normal ovary epithelium (NOE) and HGSOC were acquired in the Raman shift region from 500 to 3500 cm–1 . Raman spectra recorded vibrations of the nucleic acids, proteins, and lipids. Molecular assignments for major peaks are listed inSupplementary Table S2, according to a previous reportBy using confocal Raman spectroscopy, we first determined membrane fluidity in the tissue biopsies (0 cm–1 A using t–1) of biopsies. The spectra of ovarian specimens were composed of main peaks at 2877, 2923, and 2962 cm–1 (–1 and 2923 cm–1 (I2877/I2923) was used to indicate the ordering of the lipid phase. An increase in this ratio indicates a higher ratio of lipids in the trans conformation, which makes the membrane less fluidP<0.001.To study the membrane fluidity of the ovary specimens, we analyzed Raman spectra in the high-frequency region  in HGSOC using multiplex immunohistochemistry . Compared with NOE (n=3), the proportion of tumor biomarker PAX8, P53, CA125 and CK7- or CD44-positive cells was significantly higher in HGSOC than in NOE, while the level of tumor biomarker WT1 was higher in HGSOC than in NOE without statistical significance . Since CD44 is also expressed in immune cells, we investigated the proportion of cells expressing both the cancer biomarker CA125 and the membrane fluidity-related protein CD44 (n=6) in HGSOC. Compared with NOE (n=3), the proportion of CA125/CD44-positive cells was significantly higher in HGSOC than in NOE , which indicated a higher expression level of CD44 in HGSOC tumors (We next investigated the expression of ovarian cancer biomarkers and CD44 ( tumors .Figurein situ techniques such as confocal Raman microscopy and mHIC, we determined changes in membrane fluidity and the expression level of CD44 in stage III HGSOC. Compared with NOE, we observed an increase in membrane fluidity correlated with an increase in the CD44 level of HGSOC, which is consistent with shedding of CD44 in the cell model with decreased membrane fluidity induced by increased unsaturated fatty acidsIn the present study, with238Supplementary_Data_-12


Journal of Extracellular Vesicles, 12, e12310. https://doi.org/10.1002/jev2.12310Wang, X., Wu, R., Zhai, P., Liu, Z., Xia, R., Zhang, Z., Qin, X., Li, C., Chen, W., Li, J., & Zhang, J. (2023). Hypoxia promotes EV secretion by impairing lysosomal homeostasis in HNSCC through negative regulation of ATP6V1A by HIF‐1α. 1zjjbio@sjtu.edu.cnJianjun Zhang, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China. Email: LIJ1042@sh9hospital.org.cnJiang Li, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China. Email: chenwantao196323@sjtu.edu.cnWantao Chen, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China. Email: We apologize for this error.