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Comprehensive Research Report: SCO7099 (UniProt: Q9KZ33) - RNA Polymerase Sigma Factor from Streptomyces coelicolor

Executive Summary

SCO7099 (UniProt accession Q9KZ33) encodes a predicted extracytoplasmic function (ECF) sigma factor in Streptomyces coelicolor strain ATCC BAA-471/A3(2)/M145. Despite belonging to a well-characterized protein family, SCO7099 remains functionally uncharacterized, with no experimental data defining its regulon, promoter specificity, inducing conditions, or physiological role. The only direct experimental evidence comes from a 2024 study that failed to obtain transformants during systematic overexpression attempts, suggesting potential toxicity or regulatory constraints (lee2024machine‐learninganalysisof pages 10-11). This report synthesizes current knowledge based on domain structure predictions and comparative analysis with characterized ECF sigma factors in Streptomyces.

1. Gene and Protein Identity

1.1 Nomenclature and Organism Context

SCO7099 is the ordered locus name for this gene in S. coelicolor (ORF name: SC3A4.25c). The encoded protein is annotated as an RNA polymerase sigma factor with UniProt accession Q9KZ33 (lee2024machine‐learninganalysisof pages 10-11). Streptomyces coelicolor is a model actinobacterium notable for producing diverse secondary metabolites and exhibiting complex developmental differentiation. The organism possesses an exceptionally large sigma factor repertoire, encoding 64-65 sigma factors in its genome, far exceeding the number found in other bacteria such as Escherichia coli (7 sigma factors) or Bacillus subtilis (19 sigma factors) (lee2024machine‐learninganalysisof pages 10-11, pospisil2024σeofstreptomyces pages 1-2). The majority of these sigma factors belong to the ECF (extracytoplasmic function) family, reflecting the complex regulatory demands of Streptomyces physiology and environmental adaptation (lee2024machine‐learninganalysisof pages 10-11).

1.2 Verification of Gene Identity

The gene symbol Q9KZ33 unambiguously refers to the SCO7099 locus encoding an RNA polymerase sigma factor in S. coelicolor. This identity is consistent across UniProt annotations and genomic databases (accession NC003888.3) (lee2024machine‐learninganalysisof pages 10-11). No evidence was found for ambiguity with genes from other organisms or alternative gene products at this locus.

2. Protein Domain Architecture and Family Classification

2.1 Domain Structure

According to InterPro annotations, Q9KZ33 contains the following conserved domains:
- ECF_Sigma-70_Domain (IPR052704): The signature domain defining ECF sigma factors
- NTF2-like_dom_sf (IPR032710): A structural fold superfamily
- RNA_pol_sigma_r3/r4-like (IPR013324): Regions responsible for promoter recognition
- WH-like_DNA-bd_sf (IPR036388): Winged helix-like DNA-binding structural superfamily (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2)

2.2 ECF Sigma Factor Family

Based on its domain architecture, Q9KZ33 is classified as a member of the ECF (extracytoplasmic function) sigma factor family, also known as Group IV of the σ70 family (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2). ECF sigma factors are the largest and most diverse group of alternative sigma factors in bacteria, with 157 phylogenetically defined groups identified across bacterial genomes (dios2021extracytoplasmicfunctionσ pages 1-2).

ECF sigma factors are structurally distinguished by containing only the essential domains for RNA polymerase (RNAP) interaction and promoter recognition: the σ2 domain (responsible for -10 promoter element recognition and DNA melting) and the σ4 domain (responsible for -35 promoter element recognition), connected by a short linker of fewer than 50 residues (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2). This minimal architecture contrasts with the primary sigma factor (σ70/HrdB in Streptomyces), which contains additional regulatory domains including σ1.1 and σ1.2 (dios2021extracytoplasmicfunctionσ pages 2-4).

3. Predicted Molecular Function

3.1 General Sigma Factor Mechanism

As a sigma factor, Q9KZ33 functions as a transcription initiation specificity determinant rather than a catalytic enzyme. Sigma factors do not possess enzymatic activity themselves; instead, they serve as dissociable regulatory subunits of bacterial RNA polymerase (helmann2019wheretobegin? pages 1-3, dios2021extracytoplasmicfunctionσ pages 2-4). The core RNAP enzyme (composed of α2ββ'ω subunits) is catalytically competent for RNA synthesis but lacks promoter specificity. Upon binding a sigma factor, the RNAP holoenzyme acquires the ability to recognize specific promoter sequences, form stable promoter-RNAP complexes, and initiate transcription at defined genomic locations (helmann2019wheretobegin? pages 1-3, dios2021extracytoplasmicfunctionσ pages 2-4).

3.2 Promoter Recognition and DNA Binding

For ECF sigma factors, the σ2 domain recognizes the -10 promoter element (typically with sequence motif around TANNNT) and facilitates DNA melting to form the transcription bubble, while the σ4 domain recognizes the -35 promoter element upstream (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2). The spacing between these elements is typically 16-19 base pairs, with some ECF sigma factors showing preference for 18 bp spacers (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2). Recent structural and biochemical studies of S. coelicolor σE (another ECF sigma factor, SCO3356) revealed that its -35 motif matches the E. coli σE consensus (GGAACTT), demonstrating conservation of promoter recognition mechanisms despite limited protein sequence similarity (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2).

3.3 Substrate Specificity for SCO7099

For sigma factors, "substrate specificity" refers to promoter selectivity rather than enzymatic substrate preference. Each sigma factor recognizes a distinct set of promoter sequences, thereby controlling transcription of a specific gene set called a regulon (helmann2019wheretobegin? pages 1-3, dios2021extracytoplasmicfunctionσ pages 2-4). For SCO7099, the actual promoter consensus sequence, target genes, and regulon composition remain completely unknown (lee2024machine‐learninganalysisof pages 10-11). No ChIP-seq, transcriptomic profiling, or promoter mapping studies have identified the DNA-binding specificity or downstream targets of this sigma factor.

4. Cellular Localization

4.1 Predicted Subcellular Location

Based on domain structure and ECF sigma factor biology, Q9KZ33 is predicted to function as a cytoplasmic protein (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2). ECF sigma factors lack transmembrane domains and are not secreted; they interact with the cytoplasmic RNA polymerase core enzyme to regulate transcription of chromosomal DNA. The name "extracytoplasmic function" refers to their typical role in responding to signals originating outside the cytoplasm (cell envelope stress, extracellular signals, periplasmic events), not to their physical location (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2).

4.2 Mechanism of Localization

No experimental localization data (e.g., fluorescence microscopy, subcellular fractionation) have been reported for SCO7099. However, sigma factors are inherently cytoplasmic proteins that must access the RNA polymerase machinery associated with the nucleoid region where bacterial chromosomal DNA resides (helmann2019wheretobegin? pages 1-3, dios2021extracytoplasmicfunctionσ pages 2-4).

5. Biological Pathways and Regulatory Context

5.1 General Role of ECF Sigma Factors in Streptomyces

ECF sigma factors in Streptomyces and other actinobacteria participate in diverse stress responses and regulatory networks (pospisil2024σeofstreptomyces pages 1-2, sekurova2024deletionsofconserved pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2). Well-characterized examples include:

• σE (SCO3356): Responds to cell envelope stress (vancomycin, ethanol, osmotic stress) and regulates genes involved in cell wall biosynthesis and membrane protein export (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2)
• SigR homologs: Regulate oxidative stress response and thiol-disulfide homeostasis (sekurova2024deletionsofconserved pages 2-4)
• ECF sigma factors in secondary metabolism: Recent studies demonstrate that deletion of conserved ECF sigma factors significantly impacts secondary metabolite biosynthetic gene cluster (BGC) expression in Streptomyces (sekurova2024deletionsofconserved pages 2-4)

A 2024 machine learning analysis of 454 transcriptome profiles from S. coelicolor identified independently modulated gene groups (iModulons) and predicted putative regulons for 30 sigma factors, revealing complex cross-talk between sigma factor-mediated regulation, secondary metabolism, and stress responses involving iron and phosphate homeostasis (lee2024machine‐learninganalysisof pages 4-8).

5.2 Specific Pathway Involvement of SCO7099

No specific pathways, stress responses, or developmental processes have been experimentally linked to SCO7099 (lee2024machine‐learninganalysisof pages 10-11). The sigma factor was not among those with defined iModulons in the comprehensive transcriptomic analysis by Lee et al. (2024), suggesting either low expression levels, conditional activity under untested conditions, or functional redundancy with other sigma factors (lee2024machine‐learninganalysisof pages 4-8).

5.3 Regulation of ECF Sigma Factor Activity

ECF sigma factors are typically regulated at multiple levels (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2):

1. Anti-sigma factor sequestration: Many ECF sigma factors are held inactive by cognate anti-sigma factors, often membrane-anchored proteins that sense extracytoplasmic signals
2. Regulated proteolysis: Upon signal perception, regulated proteases (such as RseP and Prc) cleave anti-sigma factors, releasing the sigma factor
3. Transcriptional autoregulation: ECF sigma factor genes are often part of operons including their cognate anti-sigma factors, enabling positive feedback loops
4. Post-translational modification: Some sigma factors are regulated by phosphorylation or acetylation

For SCO7099, no regulatory mechanism, anti-sigma factor partner, or inducing signal has been identified (lee2024machine‐learninganalysisof pages 10-11).

6. Experimental Evidence and Current Knowledge Gaps

6.1 Limited Experimental Characterization

The only direct experimental study of SCO7099 comes from Lee et al. (2024), who conducted systematic overexpression of sigma factors in S. coelicolor to map their regulons using transcriptomics (lee2024machine‐learninganalysisof pages 10-11). In this study, 64 sigma factors were targeted for overexpression using MBP (maltose-binding protein) fusion constructs driven by the strong ermE promoter in plasmids pIBR25 (high-copy) and pSET152 (integrative, low-copy). Transformants were not obtained for three sigma factors: SCO4908 (SigQ), SCO5243 (SigH), and SCO7099* (lee2024machine‐learninganalysisof pages 10-11, lee2024machine‐learninganalysisof pages 4-8).

The authors noted that "S. coelicolor seemed to be sensitive to increased expression of sigma factors, resulting in growth failure" (lee2024machine‐learninganalysisof pages 10-11). The failure to recover transformants suggests several possible interpretations:

1. Toxicity: Overexpression may cause severe transcriptional dysregulation or competition with essential sigma factors (such as the housekeeping factor HrdB), leading to cell death
2. Cellular burden: Massive recruitment of RNAP to non-productive binding sites could deplete available RNAP
3. Essentiality or synthetic lethality: SCO7099 overexpression may interfere with essential processes or create lethal genetic interactions
4. Technical limitations: Specific barriers to transformation or plasmid maintenance unrelated to sigma factor function

Importantly, these interpretations remain speculative, as the study did not include follow-up experiments (e.g., inducible expression systems, deletion mutants, or dose-response analyses) to distinguish among these possibilities (lee2024machine‐learninganalysisof pages 10-11, lee2024machine‐learninganalysisof pages 4-8).

6.2 Absence of Other Experimental Data

No published studies have reported:
- Gene deletion or knockout phenotypes for SCO7099
- ChIP-seq or ChIP-chip mapping of DNA-binding sites
- Transcriptomic profiling of SCO7099-dependent genes
- In vitro promoter binding or transcription assays
- Protein structure determination (X-ray, cryo-EM, NMR)
- Protein-protein interaction studies to identify anti-sigma factors or other partners
- Expression pattern analysis under diverse growth conditions

This absence of data places SCO7099 among the many uncharacterized ECF sigma factors in bacteria. A recent review noted that 102 of 157 ECF phylogenetic groups still lack any mechanistic or functional insight (dios2021extracytoplasmicfunctionσ pages 1-2), highlighting the broader challenge of characterizing this diverse protein family.

6.3 Comparative Context from Other Sigma Factors

For context, well-characterized S. coelicolor ECF sigma factors have been studied using multiple complementary approaches. For example, σE (SCO3356):
- ChIP-seq identified 193 binding sites across the genome (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2)
- The regulon includes 127 genes, with enrichment for cell envelope and membrane proteins (pospisil2024σeofstreptomyces pages 2-4)
- Promoter motif analysis defined -35 (GGAACTT-like) and -10 consensus sequences (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2)
- Kinetic modeling and RT-qPCR revealed both activator and repressor functions (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2)
- Stress response was characterized under ethanol, osmotic, oxidative, and heat stress conditions (pospisil2024σeofstreptomyces pages 1-2)

None of these approaches have been applied to SCO7099, leaving its function entirely speculative (lee2024machine‐learninganalysisof pages 10-11).

7. Inferences from Domain Structure and Evolutionary Context

7.1 Structure-Based Functional Prediction

The presence of ECF_Sigma-70_Domain and RNA_pol_sigma_r3/r4-like domains strongly supports classification as a bona fide sigma factor capable of directing transcription initiation (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2). The WH-like_DNA-bd_sf (winged helix-like DNA-binding) domain is characteristic of the σ4 region that makes sequence-specific contacts with the -35 promoter element (dios2021extracytoplasmicfunctionσ pages 2-4). The NTF2-like_dom_sf may contribute to protein-protein interactions, either with RNAP core or with regulatory partners.

7.2 Evolutionary Conservation

No phylogenetic analysis or comparative genomics study specifically addressing SCO7099 was identified in the retrieved literature. However, ECF sigma factors are broadly distributed across bacterial phyla, with particular expansion in soil-dwelling actinobacteria that face diverse environmental stresses (dios2021extracytoplasmicfunctionσ pages 1-2). A 2024 study examining conserved ECF sigma factors across Streptomyces species did not specifically highlight SCO7099, suggesting it may be less highly conserved than other family members (sekurova2024deletionsofconserved pages 2-4).

8. Current Understanding and Research Priorities

8.1 Summary of Functional Annotation

Based on available evidence, the most accurate functional annotation for SCO7099 (Q9KZ33) is:

"Uncharacterized extracytoplasmic function (ECF) sigma factor; predicted transcription initiation factor capable of directing RNA polymerase to a specific but unknown set of promoters in Streptomyces coelicolor"

This annotation reflects:
- Strong structural evidence for sigma factor function (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2)
- Classification within the ECF sigma factor family (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2)
- Complete absence of experimental characterization of biological role (lee2024machine‐learninganalysisof pages 10-11)
- Failed experimental attempt at functional investigation through overexpression (lee2024machine‐learninganalysisof pages 10-11)

8.2 Priority Research Questions

To functionally annotate SCO7099, the following experimental approaches would be most informative:

1. Gene deletion analysis: Construct a clean deletion mutant and assess phenotypes under diverse stress conditions, developmental stages, and nutrient limitations
2. Conditional expression system: Use inducible promoters (e.g., tipA-thiostrepton system) to express SCO7099 at controlled levels and profile transcriptome changes by RNA-seq
3. ChIP-seq mapping: Epitope-tag SCO7099 at its native locus and map genome-wide DNA-binding sites
4. Promoter motif determination: Use in vitro transcription assays or systematic evolution of ligands by exponential enrichment (SELEX) to define promoter consensus
5. Anti-sigma factor identification: Search genomic neighborhood and perform protein interaction screens to identify regulatory partners
6. Expression profiling: Use reporter fusions (e.g., sigE-lacZ) to determine conditions under which SCO7099 is expressed
7. Comparative phenomics: Compare growth, development, and secondary metabolite production in wild-type vs. deletion strains under high-throughput conditions

8.3 Broader Context: The Challenge of Uncharacterized Sigma Factors

SCO7099 exemplifies a broader challenge in bacterial genomics: the gap between sequence-based annotation and functional understanding. Despite powerful bioinformatic predictions, experimental validation remains essential for assigning biological roles. The expansion of ECF sigma factors in Streptomyces genomes—with 64 paralogs in S. coelicolor alone—suggests extensive regulatory specialization, but also presents a daunting task for comprehensive functional characterization (lee2024machine‐learninganalysisof pages 10-11, dios2021extracytoplasmicfunctionσ pages 1-2).

Recent advances in machine learning approaches to transcriptome analysis, high-throughput ChIP-seq, and synthetic biology tools for Streptomyces genetics provide new opportunities to systematically characterize these orphan regulators (lee2024machine‐learninganalysisof pages 4-8, sekurova2024deletionsofconserved pages 2-4). Until such studies are performed for SCO7099, its function remains a matter of inference rather than empirical knowledge.

Table: This table summarizes what is currently known and not known about SCO7099 (UniProt Q9KZ33) in Streptomyces coelicolor. It highlights that the protein is strongly predicted to be an ECF sigma factor, but remains experimentally uncharacterized beyond a reported failure to recover overexpression transformants.

9. Conclusions

SCO7099 (UniProt Q9KZ33) encodes a predicted ECF sigma factor in Streptomyces coelicolor with a domain architecture consistent with transcription initiation function. Based on structural features, the protein is expected to function as a cytoplasmic regulator that directs RNA polymerase to a specific subset of promoters, likely in response to environmental or developmental signals. However, no experimental data define its regulon, promoter specificity, inducing conditions, regulatory partners, or physiological role.

The only experimental study directly addressing SCO7099 reported failure to obtain overexpression transformants, suggesting potential toxicity or regulatory constraints but providing no functional insights (lee2024machine‐learninganalysisof pages 10-11). Functional annotation of SCO7099 remains a priority for understanding the regulatory complexity of Streptomyces coelicolor, particularly given the organism's medical and biotechnological importance.

References

Primary sources cited throughout this report are indicated by context IDs (lee2024machine‐learninganalysisof pages 11-12, dios2021extracytoplasmicfunctionσ pages 1-2), corresponding to:
- Lee et al. (2024) - Machine learning analysis of S. coelicolor transcriptomes (lee2024machine‐learninganalysisof pages 10-11, lee2024machine‐learninganalysisof pages 4-8)
- Pospíšil et al. (2024) - σE functional characterization in S. coelicolor (pospisil2024σeofstreptomyces pages 2-4, pospisil2024σeofstreptomyces pages 1-2)
- Sekurova et al. (2024) - ECF sigma factor deletions and secondary metabolism (sekurova2024deletionsofconserved pages 2-4)
- Helmann (2019) - Sigma factors and transcription initiation (helmann2019wheretobegin? pages 1-3)
- de Dios et al. (2021) - ECF sigma factors as stress response coordinators (dios2021extracytoplasmicfunctionσ pages 2-4, dios2021extracytoplasmicfunctionσ pages 1-2)

References

1. (lee2024machine‐learninganalysisof pages 10-11): Yongjae Lee, Donghui Choe, Bernhard O. Palsson, and Byung‐Kwan Cho. Machine‐learning analysis of streptomyces coelicolor transcriptomes reveals a transcription regulatory network encompassing biosynthetic gene clusters. Advanced Science, Sep 2024. URL: https://doi.org/10.1002/advs.202403912, doi:10.1002/advs.202403912. This article has 12 citations and is from a peer-reviewed journal.

2. (pospisil2024σeofstreptomyces pages 1-2): Jiří Pospíšil, Marek Schwarz, Alice Ziková, Dragana Vítovská, Miluše Hradilová, Michal Kolář, Alena Křenková, Martin Hubálek, Libor Krásný, and Jiří Vohradský. Σe of streptomyces coelicolor can function both as a direct activator or repressor of transcription. Communications Biology, Jan 2024. URL: https://doi.org/10.1038/s42003-023-05716-y, doi:10.1038/s42003-023-05716-y. This article has 7 citations and is from a peer-reviewed journal.

3. (dios2021extracytoplasmicfunctionσ pages 2-4): Rubén de Dios, Eduardo Santero, and Francisca Reyes-Ramírez. Extracytoplasmic function σ factors as tools for coordinating stress responses. International Journal of Molecular Sciences, 22:3900, Apr 2021. URL: https://doi.org/10.3390/ijms22083900, doi:10.3390/ijms22083900. This article has 29 citations.

4. (dios2021extracytoplasmicfunctionσ pages 1-2): Rubén de Dios, Eduardo Santero, and Francisca Reyes-Ramírez. Extracytoplasmic function σ factors as tools for coordinating stress responses. International Journal of Molecular Sciences, 22:3900, Apr 2021. URL: https://doi.org/10.3390/ijms22083900, doi:10.3390/ijms22083900. This article has 29 citations.

5. (helmann2019wheretobegin? pages 1-3): John D. Helmann. Where to begin? sigma factors and the selectivity of transcription initiation in bacteria. Molecular Microbiology, 112:335-347, Jun 2019. URL: https://doi.org/10.1111/mmi.14309, doi:10.1111/mmi.14309. This article has 105 citations and is from a domain leading peer-reviewed journal.

6. (pospisil2024σeofstreptomyces pages 2-4): Jiří Pospíšil, Marek Schwarz, Alice Ziková, Dragana Vítovská, Miluše Hradilová, Michal Kolář, Alena Křenková, Martin Hubálek, Libor Krásný, and Jiří Vohradský. Σe of streptomyces coelicolor can function both as a direct activator or repressor of transcription. Communications Biology, Jan 2024. URL: https://doi.org/10.1038/s42003-023-05716-y, doi:10.1038/s42003-023-05716-y. This article has 7 citations and is from a peer-reviewed journal.

7. (sekurova2024deletionsofconserved pages 2-4): Olga N. Sekurova, Martin Zehl, Michael Predl, Peter Hunyadi, Thomas Rattei, and Sergey B. Zotchev. Deletions of conserved extracytoplasmic function sigma factors-encoding genes in streptomyces have a major impact on secondary metabolism. Microbial Cell Factories, Jul 2024. URL: https://doi.org/10.1186/s12934-024-02479-x, doi:10.1186/s12934-024-02479-x. This article has 1 citations and is from a peer-reviewed journal.

8. (lee2024machine‐learninganalysisof pages 4-8): Yongjae Lee, Donghui Choe, Bernhard O. Palsson, and Byung‐Kwan Cho. Machine‐learning analysis of streptomyces coelicolor transcriptomes reveals a transcription regulatory network encompassing biosynthetic gene clusters. Advanced Science, Sep 2024. URL: https://doi.org/10.1002/advs.202403912, doi:10.1002/advs.202403912. This article has 12 citations and is from a peer-reviewed journal.

9. (lee2024machine‐learninganalysisof pages 11-12): Yongjae Lee, Donghui Choe, Bernhard O. Palsson, and Byung‐Kwan Cho. Machine‐learning analysis of streptomyces coelicolor transcriptomes reveals a transcription regulatory network encompassing biosynthetic gene clusters. Advanced Science, Sep 2024. URL: https://doi.org/10.1002/advs.202403912, doi:10.1002/advs.202403912. This article has 12 citations and is from a peer-reviewed journal.

Artifacts

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Citations

1. sekurova2024deletionsofconserved pages 2-4
2. https://doi.org/10.1002/advs.202403912,
3. https://doi.org/10.1038/s42003-023-05716-y,
4. https://doi.org/10.3390/ijms22083900,
5. https://doi.org/10.1111/mmi.14309,
6. https://doi.org/10.1186/s12934-024-02479-x,