A type II toxin-antitoxin system is responsible for the cell death at low temperature in Pseudomonas syringae Lz4W lacking RNase R.

RNase R (encoded by the rnr gene) is a highly processive 3' → 5' exoribonuclease essential for the growth of the psychrotrophic bacterium Pseudomonas syringae Lz4W at low temperature. The cell death of a rnr deletion mutant at low temperature has been previously attributed to processing defects in 16S rRNA, defective ribosomal assembly, and inefficient protein synthesis. We recently showed that RNase R is required to protect P. syringae Lz4W from DNA damage and oxidative stress, independent of its exoribonuclease activity. Here, we show that the processing defect in 16S rRNA does not cause cell death of the rnr mutant of P. syringae at low temperature. Our results demonstrate that the rnr mutant of P. syringae Lz4W, complemented with a RNase R deficient in exoribonuclease function (RNase RD284A), is defective in 16S rRNA processing but can grow at 4 °C. This suggested that the processing defect in ribosomal RNAs is not a cause of the cold sensitivity of the rnr mutant. We further show that the rnr mutant accumulates copies of the indigenous plasmid pLz4W that bears a type II toxin-antitoxin (TA) system (P. syringae antitoxin-P. syringae toxin). This phenotype was rescued by overexpressing antitoxin psA in the rnr mutant, suggesting that activation of the type II TA system leads to cold sensitivity of the rnr mutant of P. syringae Lz4W. Here, we report a previously unknown functional relationship between the cold sensitivity of the rnr mutant and a type II TA system in P. syringae Lz4W.

Exoribonuclease RNase R protects Antarctic Pseudomonas syringae Lz4W from DNA damage and oxidative stress.

IMPORTANCE: Bacterial exoribonucleases play a crucial role in RNA maturation, degradation, quality control, and turnover. In this study, we have uncovered a previously unknown role of 3'-5' exoribonuclease RNase R of Pseudomonas syringae Lz4W in DNA damage and oxidative stress response. Here, we show that neither the exoribonuclease function of RNase R nor its association with the RNA degradosome complex is essential for this function. Interestingly, in P. syringae Lz4W, hydrolytic RNase R exhibits physiological roles similar to phosphorolytic 3'-5' exoribonuclease PNPase of E. coli. Our data suggest that during the course of evolution, mesophilic E. coli and psychrotrophic P. syringae have apparently swapped these exoribonucleases to adapt to their respective environmental growth conditions.

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN).

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network's real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

Broken replication forks trigger heritable DNA breaks in the terminus of a circular chromosome.

It was recently reported that the recBC mutants of Escherichia coli, deficient for DNA double-strand break (DSB) repair, have a decreased copy number of their terminus region. We previously showed that this deficit resulted from DNA loss after post-replicative breakage of one of the two sister-chromosome termini at cell division. A viable cell and a dead cell devoid of terminus region were thus produced and, intriguingly, the reaction was transmitted to the following generations. Using genome marker frequency profiling and observation by microscopy of specific DNA loci within the terminus, we reveal here the origin of this phenomenon. We observed that terminus DNA loss was reduced in a recA mutant by the double-strand DNA degradation activity of RecBCD. The terminus-less cell produced at the first cell division was less prone to divide than the one produced at the next generation. DNA loss was not heritable if the chromosome was linearized in the terminus and occurred at chromosome termini that were unable to segregate after replication. We propose that in a recB mutant replication fork breakage results in the persistence of a linear DNA tail attached to a circular chromosome. Segregation of the linear and circular parts of this "σ-replicating chromosome" causes terminus DNA breakage during cell division. One daughter cell inherits a truncated linear chromosome and is not viable. The other inherits a circular chromosome attached to a linear tail ending in the chromosome terminus. Replication extends this tail, while degradation of its extremity results in terminus DNA loss. Repeated generation and segregation of new σ-replicating chromosomes explains the heritability of post-replicative breakage. Our results allow us to determine that in E. coli at each generation, 18% of cells are subject to replication fork breakage at dispersed, potentially random, chromosomal locations.

Fatty acid starvation activates RelA by depleting lysine precursor pyruvate.

Bacteria undergoing nutrient starvation induce the ubiquitous stringent response, resulting in gross physiological changes that reprograms cell metabolism from fast to slow growth. The stringent response is mediated by the secondary messengers pppGpp and ppGpp collectively referred to as (p)ppGpp or 'alarmone'. In Escherichia coli, two paralogs, RelA and SpoT, synthesize (p)ppGpp. RelA is activated by amino acid starvation, whereas SpoT, which can also degrade (p)ppGpp, responds to fatty acid (FA), carbon and phosphate starvation. Here, we discover that FA starvation leads to rapid activation of RelA and reveal the underlying mechanism. We show that FA starvation leads to depletion of lysine that, in turn, leads to the accumulation of uncharged tRNALys and activation of RelA. SpoT was also activated by FA starvation but to a lower level and with a delayed kinetics. Next, we discovered that pyruvate, a precursor of lysine, is depleted by FA starvation. We also propose a mechanism that explains how FA starvation leads to pyruvate depletion. Together our results raise the possibility that RelA may be a major player under many starvation conditions previously thought to depend principally on SpoT. Interestingly, FA starvation provoked a ~100-fold increase in relA dependent ampicillin tolerance.

Division-induced DNA double strand breaks in the chromosome terminus region of Escherichia coli lacking RecBCD DNA repair enzyme.

Marker frequency analysis of the Escherichia coli recB mutant chromosome has revealed a deficit of DNA in a specific zone of the terminus, centred on the dif/TerC region. Using fluorescence microscopy of a marked chromosomal site, we show that the dif region is lost after replication completion, at the time of cell division, in one daughter cell only, and that the phenomenon is transmitted to progeny. Analysis by marker frequency and microscopy shows that the position of DNA loss is not defined by the replication fork merging point since it still occurs in the dif/TerC region when the replication fork trap is displaced in strains harbouring ectopic Ter sites. Terminus DNA loss in the recB mutant is also independent of dimer resolution by XerCD at dif and of Topo IV action close to dif. It occurs in the terminus region, at the point of inversion of the GC skew, which is also the point of convergence of specific sequence motifs like KOPS and Chi sites, regardless of whether the convergence of GC skew is at dif (wild-type) or a newly created sequence. In the absence of FtsK-driven DNA translocation, terminus DNA loss is less precisely targeted to the KOPS convergence sequence, but occurs at a similar frequency and follows the same pattern as in FtsK+ cells. Importantly, using ftsIts, ftsAts division mutants and cephalexin treated cells, we show that DNA loss of the dif region in the recB mutant is decreased by the inactivation of cell division. We propose that it results from septum-induced chromosome breakage, and largely contributes to the low viability of the recB mutant.

Mutations Affecting Potassium Import Restore the Viability of the Escherichia coli DNA Polymerase III holD Mutant.

Mutants lacking the ψ (HolD) subunit of the Escherichia coli DNA Polymerase III holoenzyme (Pol III HE) have poor viability, but a residual growth allows the isolation of spontaneous suppressor mutations that restore ΔholD mutant viability. Here we describe the isolation and characterization of two suppressor mutations in the trkA and trkE genes, involved in the main E. coli potassium import system. Viability of ΔholD trk mutants is abolished on media with low or high K+ concentrations, where alternative K+ import systems are activated, and is restored on low K+ concentrations by the inactivation of the alternative Kdp system. These findings show that the ΔholD mutant is rescued by a decrease in K+ import. The effect of trk inactivation is additive with the previously identified ΔholD suppressor mutation lexAind that blocks the SOS response indicating an SOS-independent mechanism of suppression. Accordingly, although lagging-strand synthesis is still perturbed in holD trkA mutants, the trkA mutation allows HolD-less Pol III HE to resist increased levels of the SOS-induced bypass polymerase DinB. trk inactivation is also partially additive with an ssb gene duplication, proposed to stabilize HolD-less Pol III HE by a modification of the single-stranded DNA binding protein (SSB) binding mode. We propose that lowering the intracellular K+ concentration stabilizes HolD-less Pol III HE on DNA by increasing electrostatic interactions between Pol III HE subunits, or between Pol III and DNA, directly or through a modification of the SSB binding mode; these three modes of action are not exclusive and could be additive. To our knowledge, the holD mutant provides the first example of an essential protein-DNA interaction that strongly depends on K+ import in vivo.

The inactivation of rfaP, rarA or sspA gene improves the viability of the Escherichia coli DNA polymerase III holD mutant.

The Escherichia coli holD mutant is poorly viable because the stability of holoenzyme polymerase III (Pol III HE) on DNA is compromised. Consequently, the SOS response is induced and the SOS polymerases DinB and Pol II further hinder replication. Mutations that restore the holD mutant viability belong to two classes, those that stabilize Pol III on DNA and those that prevent the deleterious effects of DinB over-production. We identified a dnaX mutation and the inactivation of rfaP and sspA genes as belonging to the first class of holD mutant suppressors. dnaX encodes a Pol III clamp loader subunit that interacts with HolD. rfaP encodes a lipopolysaccharide kinase that acts in outer membrane biogenesis. Its inactivation improves the holD mutant growth in part by affecting potassium import, previously proposed to stabilize Pol III HE on DNA by increasing electrostatic interactions. sspA encodes a global transcriptional regulator and growth of the holD mutant in its absence suggests that SspA controls genes that affect protein-DNA interactions. The inactivation of rarA belongs to the second class of suppressor mutations. rarA inactivation has a weak effect but is additive with other suppressor mutations. Our results suggest that RarA facilitates DinB binding to abandoned forks.

Replication arrest is a major threat to growth at low temperature in Antarctic Pseudomonas syringae†Lz4W.

Chromosomal damage was detected previously in the recBCD mutants of the Antarctic bacterium Pseudomonas syringae Lz4W, which accumulated linear chromosomal DNA leading to cell death and growth inhibition at 4°C. RecBCD protein generally repairs DNA double-strand breaks by RecA-dependent homologous recombination pathway. Here we show that ΔrecA mutant of P. syringae is not cold-sensitive. Significantly, inactivation of additional DNA repair genes ruvAB rescued the cold-sensitive phenotype of ΔrecBCD mutant. The ΔrecA and ΔruvAB mutants were UV-sensitive as expected. We propose that, at low temperature DNA replication encounters barriers leading to frequent replication fork (RF) arrest and fork reversal. RuvAB binds to the reversed RFs (RRFs) having Holliday junction-like structures and resolves them upon association with RuvC nuclease to cause linearization of the chromosome, a threat to cell survival. RecBCD prevents this by degrading the RRFs, and facilitates replication re-initiation. This model is consistent with our observation that low temperature-induced DNA lesions do not evoke SOS response in P. syringae. Additional studies show that two other repair genes, radA (encoding a RecA paralogue) and recF are not involved in providing cold resistance to the Antarctic bacterium.

Regulation of microbial gene expression: the key to understanding our gut microbiome.

During the past two decades, gut microbiome studies have established the significant impact of the gut microbiota and its metabolites on host health. However, the molecular mechanisms governing the production of microbial metabolites in the gut environment remain insufficiently investigated and thus are poorly understood. Here, we propose that an enhanced understanding of gut microbial gene regulation, which is responsive to dietary components and gut environmental conditions, is needed in the research field and essential for our ability to effectively promote host health and prevent diseases through interventions targeting the gut microbiome.

BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models.

Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from the World Health Organization show an alarming annual incidence of nearly 2.3 million new cases, drawing the attention of patients, healthcare professionals, and governments alike. Through the examination of histopathological pictures, this study aims to revolutionize the early and precise identification of breast cancer by utilizing the capabilities of a deep convolutional neural network (CNN)-based model. The model's performance is improved by including numerous classifiers, including support vector machine (SVM), decision tree, and K-nearest neighbors (KNN), using transfer learning techniques. The studies include evaluating two separate feature vectors, one with and one without principal component analysis (PCA). Extensive comparisons are made to measure the model's performance against current deep learning models, including critical metrics such as false positive rate, true positive rate, accuracy, precision, and recall. The data show that the SVM algorithm with PCA features achieves excellent speed and accuracy, with an amazing accuracy of 99.5%. Furthermore, although being somewhat slower than SVM, the decision tree model has the greatest accuracy of 99.4% without PCA. This study suggests a viable strategy for improving early breast cancer diagnosis, opening the path for more effective healthcare treatments and better patient outcomes.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

Dietary fibre directs microbial tryptophan metabolism via metabolic interactions in the gut microbiota.

Tryptophan is catabolized by gut microorganisms resulting in a wide range of metabolites implicated in both beneficial and adverse host effects. How gut microbial tryptophan metabolism is directed towards indole, associated with chronic kidney disease, or towards protective indolelactic acid (ILA) and indolepropionic acid (IPA) is unclear. Here we used in vitro culturing and animal experiments to assess gut microbial competition for tryptophan and the resulting metabolites in a controlled three-species defined community and in complex undefined human faecal communities. The generation of specific tryptophan-derived metabolites was not predominantly determined by the abundance of tryptophan-metabolizing bacteria, but rather by substrate-dependent regulation of specific metabolic pathways. Indole-producing Escherichia coli and ILA- and IPA-producing Clostridium sporogenes competed for tryptophan within the three-species community in vitro and in vivo. Importantly, fibre-degrading Bacteroides thetaiotaomicron affected this competition by cross-feeding monosaccharides to E. coli. This inhibited indole production through catabolite repression, thus making more tryptophan available to C. sporogenes, resulting in increased ILA and IPA production. The fibre-dependent reduction in indole was confirmed using human faecal cultures and faecal-microbiota-transplanted gnotobiotic mice. Our findings explain why consumption of fermentable fibres suppresses indole production but promotes the generation of other tryptophan metabolites associated with health benefits.

CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach.

Background and Aims: Diabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature-fusion strategy employing supervised learning classifiers. Methods: Preprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics. Results: Based on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes. Conclusion: The recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.

WHOOPH: whale optimization-based optimal placement of hub node within a WBAN.

Biosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.

Key bacterial taxa determine longitudinal dynamics of aromatic amino acid catabolism in infants' gut.

The development of the gut microbiota in early life is linked to metabolic, neuronal, and immunological development. Recent studies have shown that bacterial production of short-chain fatty acids (SCFAs) and aromatic amino acid (AAA) catabolites in the gut can mediate host-microbe interactions. However, the dynamics of these microbiota-derived metabolites and the key bacterial taxa producing AAA catabolites during infancy are largely unknown. Here, we investigated the longitudinal dynamics of the microbiota and microbiota-derived SCFAs and AAA catabolites in more than 200 fecal samples from 25 healthy breast- or mixed-fed Danish infants during the first 6 months of life. We found that the gut microbiota composition and metabolism were highly individual but showed significant development over time. SCFAs and specific groups of AAA catabolites showed distinct temporal abundance patterns. Furthermore, we identified bacterial taxa responsible for the generation of AAA catabolites by associating the dynamics of gut microbial taxa and AAA catabolites and subsequently validating these associations in vitro by cultivation of strains representing the associated taxa. In addition to specific Bifidobacterium species being the main producers of aromatic lactic acids, we identified Peptostreptococcus anaerobius as the main producer of aromatic propionic acids, Ruminococcus gnavus as a main producer of tryptamine, and Enterococcus species as main tyramine producers in infants' gut. Thus, our results showcase the temporal dynamics of key gut microbial metabolites in early life and demonstrate that the appearance and abundance of specific AAA catabolites result from the appearance and abundance of specific key bacterial taxa in infants' gut.

The RelA hydrolase domain acts as a molecular switch for (p)ppGpp synthesis.

Bacteria synthesize guanosine tetra- and penta phosphate (commonly referred to as (p)ppGpp) in response to environmental stresses. (p)ppGpp reprograms cell physiology and is essential for stress survival, virulence and antibiotic tolerance. Proteins of the RSH superfamily (RelA/SpoT Homologues) are ubiquitously distributed and hydrolyze or synthesize (p)ppGpp. Structural studies have suggested that the shift between hydrolysis and synthesis is governed by conformational antagonism between the two active sites in RSHs. RelA proteins of γ-proteobacteria exclusively synthesize (p)ppGpp and encode an inactive pseudo-hydrolase domain. Escherichia coli RelA synthesizes (p)ppGpp in response to amino acid starvation with cognate uncharged tRNA at the ribosomal A-site, however, mechanistic details to the regulation of the enzymatic activity remain elusive. Here, we show a role of the enzymatically inactive hydrolase domain in modulating the activity of the synthetase domain of RelA. Using mutagenesis screening and functional studies, we identify a loop region (residues 114-130) in the hydrolase domain, which controls the synthetase activity. We show that a synthetase-inactive loop mutant of RelA is not affected for tRNA binding, but binds the ribosome less efficiently than wild type RelA. Our data support the model that the hydrolase domain acts as a molecular switch to regulate the synthetase activity.

Asphaltene biotransformation for heavy oil upgradation.

Globally, the reserves of heavy crude oil are seven times more abundant than that of light crude, and yet, they are underutilized because of their high viscosity and density, which is largely due to the presence of large amounts of asphaltenes. Biotransformation of heavy oil asphaltenes into smaller metabolites can be used for reducing their viscosity. Several microorganisms capable of asphaltene biodegradation have been reported but only few have been characterized for its biotransformation. In the present study, a 9-membered microbial consortium was isolated from an oil contaminated soil. About 72% and 75% asphaltene biotransformation was achieved by growing cells at shake flask level and in a 1.5 l bioreactor, respectively. A representative structure of asphaltene was constructed based on LC-MS, 1H-NMR, 13C-NMR, FT-IR, ICPMS and elemental analysis (CHNS) of n-heptane purified asphaltene from Maya crude oil. Biotransformation of asphaltene, as analyzed by performing 1H-NMR, FT-IR and elemental analysis, resulted in 80% decrease in S and N when compared to the control along with incorporation of oxygen in the structure of asphaltene. About 91% decrease in the viscosity of the Maya crude oil was observed after two weeks when oil: aqueous phase ratio was 1:9. The results suggest that the isolated microbial consortium can be used for biological upgradation of heavy crude oil. To our knowledge, this is the first report where a microbial consortium resulted in such high asphaltene biotransformation.

Bacterial Chromosome Replication and DNA Repair During the Stringent Response.

The stringent response regulates bacterial growth rate and is important for cell survival under changing environmental conditions. The effect of the stringent response is pleiotropic, affecting almost all biological processes in the cell including transcriptional downregulation of genes involved in stable RNA synthesis, DNA replication, and metabolic pathways, as well as the upregulation of stress-related genes. In this Review, we discuss how the stringent response affects chromosome replication and DNA repair activities in bacteria. Importantly, we address how accumulation of (p)ppGpp during the stringent response shuts down chromosome replication using highly different strategies in the evolutionary distant Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Interestingly, (p)ppGpp-mediated replication inhibition occurs downstream of the origin in B. subtilis, whereas replication inhibition in E. coli takes place at the initiation level, suggesting that stringent cell cycle arrest acts at different phases of the replication cycle between E. coli and B. subtilis. Furthermore, we address the role of (p)ppGpp in facilitating DNA repair activities and cell survival during exposure to UV and other DNA damaging agents. In particular, (p)ppGpp seems to stimulate the efficiency of nucleotide excision repair (NER)-dependent repair of DNA lesions. Finally, we discuss whether (p)ppGpp-mediated cell survival during DNA damage is related to the ability of (p)ppGpp accumulation to inhibit chromosome replication.