Compound Deletion of Thrombospondin-1 and -2 Results in a Skeletal Phenotype not Predicted by the Single Gene Knockouts

Abstract Generating cellular Ca2+ signals requires coordinated transport activities from both Ca2+ influx and efflux pathways. In Arabidopsis (Arabidopsis thaliana), multiple efflux pathways exist, some of which involve Ca2+-pumps belonging to the Autoinhibited Ca2+-ATPase (ACA) family. Here we show that ACA1, 2, and 7 localize to the endoplasmic reticulum (ER) and are important for plant growth and pollen fertility. While phenotypes for plants harboring single gene knockouts (KOs) were weak or undetected, a triple KO of aca1/2/7 displayed a 2.6-fold decrease in pollen transmission efficiency, whereas inheritance through female gametes was normal. The triple KO also resulted in smaller rosettes showing a high frequency of lesions. Both vegetative and reproductive phenotypes were rescued by transgenes encoding either ACA1, 2, or 7, suggesting that all three isoforms are biochemically redundant. Lesions were suppressed by expression of a transgene encoding NahG, an enzyme that degrades salicylic acid (SA). Triple KO mutants showed elevated mRNA expression for two SA-inducible marker genes, PR1 (Pathogenesis-related 1) and PR2. The aca1/2/7 lesion phenotype was similar but less severe than SA-dependent lesions associated with a double KO of vacuolar pumps aca4 and 11. Imaging of Ca2+ dynamics triggered by blue light or the pathogen elicitor flg22 revealed that aca1/2/7 mutants display Ca2+ transients with increased magnitudes and durations. Together, these results indicate that ER-localized ACAs play important roles in regulating Ca2+ signals, and that the loss of these pumps results in male fertility and vegetative growth deficiencies.

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Understanding metabolic behaviour in whole-cell model output

10.1101/2020.08.19.257147 ◽

2020 ◽

Author(s):

Sophie Landon ◽

Oliver Chalkley ◽

Gus Breese ◽

Claire Grierson ◽

Lucia Marucci

Keyword(s):

Drug Testing ◽

Graphical Representation ◽

Cell Model ◽

Single Gene ◽

Detailed Knowledge ◽

Metabolic Reaction ◽

Model Output ◽

Analysis Pipeline ◽

Whole Cell ◽

Gene Knockouts

SummaryWhole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional, thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts, and, as whole-cell models become more widely built and used, aid genome design.

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Extending homologous sequence based on the single gene mutants by one-step PCR for efficient multiple gene knockouts

Folia Microbiologica ◽

10.1007/s12223-012-0111-z ◽

2012 ◽

Vol 57 (3) ◽

pp. 209-214 ◽

Cited By ~ 9

Author(s):

Mingji Li ◽

Pengfei Gu ◽

Junhua Kang ◽

Yang Wang ◽

Qian Wang ◽

...

Keyword(s):

Single Gene ◽

Homologous Sequence ◽

Multiple Gene ◽

One Step ◽

Gene Knockouts

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Innate responses to gene knockouts impact overlapping gene networks and vary with respect to resistance to viral infection

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1720464115 ◽

2018 ◽

Vol 115 (14) ◽

pp. E3230-E3237 ◽

Cited By ~ 7

Author(s):

Yonghong Liu ◽

Yuanyuan Liu ◽

Jiaming Wu ◽

Bernard Roizman ◽

Grace Guoying Zhou

Keyword(s):

Cell Lines ◽

Gene Networks ◽

Single Gene ◽

Parental Cell ◽

Parental Cell Line ◽

Gene Encoding ◽

Overlapping Gene ◽

Gene Knockouts ◽

Cluster 2 ◽

Hsv 1

Analyses of the levels of mRNAs encoding IFIT1, IFI16, RIG-1, MDA5, CXCL10, LGP2, PUM1, LSD1, STING, and IFNβ in cell lines from which the gene encoding LGP2, LSD1, PML, HDAC4, IFI16, PUM1, STING, MDA5, IRF3, or HDAC 1 had been knocked out, as well as the ability of these cell lines to support the replication of HSV-1, revealed the following: (i) Cell lines lacking the gene encoding LGP2, PML, or HDAC4 (cluster 1) exhibited increased levels of expression of partially overlapping gene networks. Concurrently, these cell lines produced from 5 fold to 12 fold lower yields of HSV-1 than the parental cells. (ii) Cell lines lacking the genes encoding STING, LSD1, MDA5, IRF3, or HDAC 1 (cluster 2) exhibited decreased levels of mRNAs of partially overlapping gene networks. Concurrently, these cell lines produced virus yields that did not differ from those produced by the parental cell line. The genes up-regulated in cell lines forming cluster 1, overlapped in part with genes down-regulated in cluster 2. The key conclusions are that gene knockouts and subsequent selection for growth causes changes in expression of multiple genes, and hence the phenotype of the cell lines cannot be ascribed to a single gene; the patterns of gene expression may be shared by multiple knockouts; and the enhanced immunity to viral replication by cluster 1 knockout cell lines but not by cluster 2 cell lines suggests that in parental cells, the expression of innate resistance to infection is specifically repressed.

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Thermoneutrality improves skeletal impairment in adult Prader–Willi syndrome mice

Journal of Endocrinology ◽

10.1530/joe-19-0279 ◽

2019 ◽

Vol 243 (3) ◽

pp. 175-186

Author(s):

Thomas M Braxton ◽

Dionne E A Sarpong ◽

Janine L Dovey ◽

Anne Guillou ◽

Bronwen A J Evans ◽

...

Keyword(s):

Single Gene ◽

Bone Geometry ◽

Prader Willi Syndrome ◽

Multiple Systems ◽

Marrow Adiposity ◽

Impaired Limb ◽

Calcified Tissue ◽

Skeletal Phenotype ◽

Skeletal Integrity ◽

The Impact

Human Prader–Willi syndrome (PWS) is characterised by impairments of multiple systems including the growth hormone (GH) axis and skeletal growth. To address our lack of knowledge of the influence of PWS on skeletal integrity in mice, we have characterised the endocrine and skeletal phenotype of the PWS-IC del mouse model for ‘full’ PWS and determined the impact of thermoneutrality. Tibial length, epiphyseal plate width and marrow adiposity were reduced by 6, 18 and 79% in male PWS-IC del mice, with osteoclast density being unaffected. Similar reductions in femoral length accompanied a 32% reduction in mid-diaphyseal cortical diameter. Distal femoral Tb.N was reduced by 62%, with individual trabeculae being less plate-like and the lattice being more fragmented (Tb.Pf increased by 63%). Cortical strength (ultimate moment) was reduced by 26% as a result of reductions in calcified tissue strength and the geometric contribution. GH and prolactin contents in PWS-IC del pituitaries were reduced in proportion to their smaller pituitary size, with circulating IGF-1 concentration reduced by 37–47%. Conversely, while pituitary luteinising hormone content was halved, circulating gonadotropin concentrations were unaffected. Although longitudinal growth, marrow adiposity and femoral geometry were unaffected by thermoneutrality, strengthened calcified tissue reversed the weakened cortex of PWS-IC del femora. While underactivity of the GH axis may be due to loss of Snord116 expression and impaired limb bone geometry and strength due to loss of Magel2 expression, comprehensive analysis of skeletal integrity in the single gene deletion models is required. Our data imply that thermoneutrality may ameliorate the elevated fracture risk associated with PWS.

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Determination of Antibiotic Hypersensitivity among 4,000 Single-Gene-Knockout Mutants of Escherichia coli

Journal of Bacteriology ◽

10.1128/jb.01982-07 ◽

2008 ◽

Vol 190 (17) ◽

pp. 5981-5988 ◽

Cited By ~ 143

Author(s):

Cindy Tamae ◽

Anne Liu ◽

Katherine Kim ◽

Daniel Sitz ◽

Jeeyoon Hong ◽

...

Keyword(s):

Escherichia Coli ◽

High Throughput Screening ◽

Gene Knockout ◽

Single Gene ◽

E Coli ◽

Knockout Mutants ◽

Data Points ◽

Increased Sensitivity ◽

Gene Knockouts

ABSTRACT We have tested the entire Keio collection of close to 4,000 single-gene knockouts in Escherichia coli for increased susceptibility to one of seven different antibiotics (ciprofloxacin, rifampin, vancomycin, ampicillin, sulfamethoxazole, gentamicin, or metronidazole). We used high-throughput screening of several subinhibitory concentrations of each antibiotic and reduced more than 65,000 data points to a set of 140 strains that display significantly increased sensitivities to at least one of the antibiotics, determining the MIC in each case. These data provide targets for the design of “codrugs” that can potentiate existing antibiotics. We have made a number of double mutants with greatly increased sensitivity to ciprofloxacin, and these overcome the resistance generated by certain gyrA mutations. Many of the gene knockouts in E. coli are hypersensitive to more than one antibiotic. Together, all of these data allow us to outline the cell's “intrinsic resistome,” which provides innate resistance to antibiotics.

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Genetic analysis of tellurate reduction reveals the selenate/tellurate reductase genes ynfEF and the transcriptional regulation of moeA by NsrR in Escherichia coli

The Journal of Biochemistry ◽

10.1093/jb/mvaa120 ◽

2020 ◽

Author(s):

Daiki Fujita ◽

Ryuta Tobe ◽

Hirotaka Tajima ◽

Yukari Anma ◽

Ryo Nishida ◽

...

Keyword(s):

Escherichia Coli ◽

Single Gene ◽

E Coli ◽

Twin Arginine Translocation ◽

Iron Sulfur ◽

Nitric Oxide Sensor ◽

Biosynthesis Gene ◽

Gene Knockouts ◽

Translocation Pathway ◽

Keio Collection

Abstract Several bacteria can reduce tellurate into the less toxic elemental tellurium, but the genes responsible for this process have not yet been identified. In this study, we screened the Keio collection of single-gene knockouts of Escherichia coli responsible for decreased tellurate reduction and found that deletions of 29 genes, including those for molybdenum cofactor (Moco) biosynthesis, iron-sulfur biosynthesis, and the twin-arginine translocation pathway resulted in decreased tellurate reduction. Among the gene knockouts, deletions of nsrR, moeA, yjbB, ynbA, ydaS, and yidH affected tellurate reduction more severely than those of other genes. Based on our findings, we determined that the ynfEF genes, which code for the components of the selenate reductase YnfEFGH, are responsible for tellurate reduction. Assays of several molybdoenzymes in the knockouts suggested that nsrR, yjbB, ynbA, ydaS, and yidH are essential for the activities of molybdoenzymes in E. coli. Furthermore, we found that the nitric oxide sensor NsrR positively regulated the transcription of the Moco biosynthesis gene moeA. These findings provided new insights into the complexity and regulation of Moco biosynthesis in E. coli.

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Identification of Escherichia coli Host Genes That Influence the Bacteriophage Lambda (λ) T4rII Exclusion (Rex) Phenotype

Genetics ◽

10.1534/genetics.120.303643 ◽

2020 ◽

Vol 216 (4) ◽

pp. 1087-1102

Author(s):

Hibah Alattas ◽

Shirley Wong ◽

Roderick A. Slavcev

Keyword(s):

Escherichia Coli ◽

Stationary Phase ◽

Bacteriophage Lambda ◽

Single Gene ◽

Cellular Environment ◽

Osmotic Balance ◽

One Step ◽

Gene Knockouts ◽

First Time ◽

Host Genes

The T4rII exclusion (Rex) phenotype is the inability of T4rII mutant bacteriophage to propagate in hosts (Escherichia coli) lysogenized by bacteriophage lambda (λ). The Rex phenotype, triggered by T4rII infection of a rex+ λ lysogen, results in rapid membrane depolarization imposing a harsh cellular environment that resembles stationary phase. Rex “activation” has been proposed as an altruistic cell death system to protect the λ prophage and its host from T4rII superinfection. Although well studied for over 60 years, the mechanism behind Rex still remains unclear. We have identified key nonessential genes involved in this enigmatic exclusion system by examining T4rII infection across a collection of rex+ single-gene knockouts. We further developed a system for rapid, one-step isolation of host mutations that could attenuate/abrogate the Rex phenotype. For the first time, we identified host mutations that influence Rex activity and rex+ host sensitivity to T4rII infection. Among others, notable genes include tolA, ompA, ompF, ompW, ompX, ompT, lpp, mglC, and rpoS. They are critical players in cellular osmotic balance and are part of the stationary phase and/or membrane distress regulons. Based on these findings, we propose a new model that connects Rex to the σS, σE regulons and key membrane proteins.

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Understanding Metabolic Flux Behaviour in Whole-Cell Model Output

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.732079 ◽

2021 ◽

Vol 8 ◽

Author(s):

Sophie Landon ◽

Oliver Chalkley ◽

Gus Breese ◽

Claire Grierson ◽

Lucia Marucci

Keyword(s):

Metabolic Flux ◽

Graphical Representation ◽

Cell Model ◽

Single Gene ◽

Detailed Knowledge ◽

Metabolic Reaction ◽

Model Output ◽

Analysis Pipeline ◽

Whole Cell ◽

Gene Knockouts

Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.

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