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Host Range

The host range of a biological entity, such as a virus, bacteria, or parasite, refers to the set of host organisms that it is capable of infecting or colonizing.
This term provides a concise overview of the diversity of potential hosts for a given pathogen or organism.
Understanding the host range is crucial for epidemiology, disease prevention, and developing targeted interventions.
PubCompare.ai's innovative AI-driven platform can help researchers optimize their work by accurately identifying and comparing host range information from published literature, preprints, and patents.
Leveraging this tool can minimize errors and enhance the reproducibility of research in this critical domain.

Most cited protocols related to «Host Range»

1
Comprehensive Phage Host Range Evaluation
The six phages that displayed the widest bactericide host range in the spot assays were selected for a more thorough assessment of productive infection as defined by the efficiency of plating (EOP). Each phage was tested three times for each of four different dilutions against all the bacterial strains that it had been shown to be able to lyse in the spot assays. This was done under the same conditions as in the spot assays, i.e. using stationary phase bacteria. Thus, all bacterial strains to be tested were grown overnight (18 hours) at 30°C and 200 μl of each of those cultures was used in double layer plaque assays together with 100 μl of diluted phage lysate. The four phage lysates were 106–109 times dilutions from the phage stock. This means that EOP assay replicates for a particular phage were done in parallel on all bacterial strains tested, and also at concentrations comparable to what was used in the spot tests. The plates were incubated overnight at 30°C and the number of plaque forming units (PFU) was counted for each combination. When the 106 dilution did not result in any plaques, a lower dilution was tried afterwards to verify that the EOP was lower than 0.001. Finally, the EOP was calculated (average PFU on target bacteria / average PFU on host bacteria) along with the standard deviation for the three measurements (S1 Table).
The average EOP value for a particular phage—bacterium combination was classified as “High production” when the ratio was 0.5 or more, i.e. when the productive infection on the target bacterium resulted in at least 50% of the PFU found for the primary host. An EOP of 0.1 or better, but below 0.5, was considered to be of “Medium production” efficiency, and between 0.001 and 0.1 as “Low production” efficiency. An EOP equal to or under 0.001 was classified as inefficient [34 (link)].
Khan Mirzaei M, & Nilsson A.S. (2015). Isolation of Phages for Phage Therapy: A Comparison of Spot Tests and Efficiency of Plating Analyses for Determination of Host Range and Efficacy. PLoS ONE, 10(3), e0118557.
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Publication 2015
Bacteria Bacterial Infections Bacteriophages Biological Assay Dental Plaque Host Range Infection Senile Plaques Strains Technique, Dilution
2
Isolation and Characterization of Sewage Phages
The bacteriophages were isolated from sewage water sampled at the Käppala waste water treatment plant (location: WGS84: 59°21’22.2"N 18°13’45.3"E), recipient of waste water from Stockholm city including some hospitals, and kept at +4°C before processing. The sampling of water was approved by the owner, Käppalaförbundet, Box 3095, 181 03 Lidingö, Sweden, kappala@kappala.se (Phages are not considered to be protected species). The phages were amplified by mixing 50 ml of waste water with the same amount of double strength LB and 10 ml of a single ECOR strain bacteria cultured overnight. After incubation overnight at 30°C, 10 ml of the mixture was shaken with 1% v/v chloroform and left at room temperature for 30 minutes to kill the bacteria, centrifuged at 3000×g at +4°C for 15 minutes, and sterile filtered through a 0.45 μm membrane filter (Whatman, ref. no. 10462100). After checking the lysates for phages, the titre was measured in plaque assays. Sterile filtered phage lysates were diluted in SM buffer [30 ] or LB to five different dilutions (10-5–10-9). 100 μl of diluted phage and 200 μl of target bacteria were mixed with 2 ml SA, spread on pre-warmed LA plates, and incubated overnight at 30°C [31 (link)]. The harvested phages were selected according to their plaque morphology. Phages displaying large, clear and non-turbid plaques without a halo were classified as virulent. Phages were re-isolated by plaque purification from the LA plates when several phages on the same plate could be suspected. After additional plaque purifications, 25 virulent phages were saved and stored in 50% glycerol at -70°C as well as in LB at +4°C [32 (link)]. These phages were named according to guidelines in Kropinski et al. [33 (link)]. The six phages showing the broadest host range in the spot test (see below) were thus named as follows; vB_EcoP_SU10, vB_EcoP_SU16, vB_EcoP_SU27, vB_EcoP_SU32, vB_EcoP_SU57, and vB_EcoP_SU63, but only the last part of names is used in the following text, e.g. SU10, SU16 and so on.
Khan Mirzaei M, & Nilsson A.S. (2015). Isolation of Phages for Phage Therapy: A Comparison of Spot Tests and Efficiency of Plating Analyses for Determination of Host Range and Efficacy. PLoS ONE, 10(3), e0118557.
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Publication 2015
Bacteria Bacteriophages Biological Assay Buffers Chloroform Dental Plaque Glycerin Host Range isolation & purification Plants Senile Plaques Sewage Sterility, Reproductive Technique, Dilution Tissue, Membrane
3
Genetically Engineered L. pneumophila Strains
LP02 is a streptomycin-resistant thymidine auxotroph derived from L. pneumophila LP01. An unmarked deletion of flaA (corresponding to nucleotides 1478105–1479574 of the LP01 genome [49 (link)]) was generated in LP02 by use of the allelic exchange vector pSR47S [22 (link)]. The ΔflaA strain was also complemented by inserting the flaA gene and its own promoter (corresponding to nucleotides 1478136–1479915 of the LP01 genome) on the chromosome just after the ahpC gene (at position 3354877 of the LP01 genome). The ahpC locus is highly expressed but is not essential for Legionella virulence [50 (link)]. The fliI null strain LP02 fliI::Cm [22 (link)] was the kind gift of R. Isberg (Tufts University School of Medicine). The broad-host-range plasmid pBBR1-MCS2 was used to express flagellin from Legionella, E. coli MG1655 (fliC, b1923), S. flexneri 2457T (fliC, S2062), and Salmonella typhimurium LT2 (fliC, STM1959). The various flagellin open reading frames were first cloned into pET28a (NcoI, XhoI) and then transferred to pBBR1-MCS2 (XbaI, PvuI), such that all flagellins were expressed from the lac promoter of pBBR and the ribosome-binding site of pET28a. Expression was induced with 1mM IPTG. The pBBR-flagellin constructs were also expressed in E. coli CM735ΔfliC [51 (link)], along with LLO (pACYC184-LLOc) [41 (link)]. Salmonella strains were on the LT2 background and were the kind gift of the Starnbach laboratory.
Ren T., Zamboni D.S., Roy C.R., Dietrich W.F, & Vance R.E. (2006). Flagellin-Deficient Legionella Mutants Evade Caspase-1- and Naip5-Mediated Macrophage Immunity. PLoS Pathogens, 2(3), e18.
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Publication 2006
Alleles Binding Sites Chromosomes Cloning Vectors Deletion Mutation Escherichia coli Flagellin Genes Genome Host Range Isopropyl Thiogalactoside Legionella Nucleotides Open Reading Frames Plasmids Ribosomes Salmonella Salmonella typhimurium LT2 Strains Streptomycin Thymidine Virulence
4
Similarity Search for 16S rRNA Sequences
To initiate a similarity query, a target sequence of interest is needed as template. Full or nearly-full length 16S rRNA gene sequence is recommended to ensure good coverage across all studies independent of 16S rRNA gene regions. For the purpose of demonstration, we used the 16S rRNA gene sequence of Acetatifactor muris (GenBank accession HM989805). In the Query/Similarity tab in IMNGS, we entered the FASTA formatted sequence of A. muris, selected the “Gut samples” as target list, and 95% as the sequence similarity threshold for query. Similarity queries are performed by IMNGS with UBLAST36 (link) over the preformatted databases of sequences. Depending on the number of selected samples to be searched (all or lists), a similarity query can take up to 6 hours until completion. Results were downloaded from the Jobs tab as a compressed folder containing all output and information files (see Supplementary File S1 for detailed description of output files). Since the purpose of this demo search was to determine the host range of colonization without consideration of relative sequence abundances, the “report.0.tab” file was used, i.e. the report of positive samples per sample category (e.g. marine water, human skin, etc.) where a single hit is sufficient for a sample to be considered as positive. Different levels of relative abundances are offered to users, excluding rare (>0.1% threshold) or including only abundant (>1%) sequences. Detailed information on individual hits can be extracted from the “*.hits.tab” file, containing for instance sample type and matched sequence numbers for each query, and all hit sequences are available in “*.seqs.fasta” (the asterisk indicates the numbering of the sequence used as query).
Lagkouvardos I., Joseph D., Kapfhammer M., Giritli S., Horn M., Haller D, & Clavel T. (2016). IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Scientific Reports, 6, 33721.
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Publication 2016
Acetatifactor muris Base Sequence Genes Homo sapiens Host Range Marines Ribosomal RNA Genes RNA, Ribosomal, 16S Skin
5
Phylogenetic Analysis of Bartonella Isolates
Phylogenetic analysis and alignments were carried out using the Mega 4.1 software [39 (link)], but examination of resulting cladograms revealed poor resolution of clades at a subspecific level. A network of zero-step and one-step clades was therefore established using the methodology of Nested Clade Analysis and statistical parsimony [41 (link)–43 (link)] to describe the most likely mutational relationships between Bartonella isolates collected during this work. The cladogram and the limit for statistical parsimony were calculated using TCS [10 (link)]. Four isolates, EU014267, EU014269, EU014274 and EU014275, collected from the same location by Welc-Falęciak et al. [44 (link)] in 2005, were also included because they represented otherwise missing internal steps within the clade network. Log-linear models were implemented using SPSS v 14.00 to establish significant departures from randomness in the host range of isolates within each nested Bartonella clade [41 (link)]. To identify recombination within the gltA gene, the sequenced fragment was first divided into three 100 bp segments and phylogenies generated using the minimum evolution algorithm in Mega 4.1. Discrepancies between these phylogenies were then used to identify potential recombinant gltA sequences, which were analysed further to confirm or reject recombination using the RDP-2 software package [18 (link)]. To identify potential recombination events between disparate parts of the genome, isolates from the range of Bartonella gltA clades were sequenced at the other genes described. All distinct genotypes of each gene were treated as distinct alleles and coded as such. Using an MLST approach [14 (link), 29 (link)], distinct alleles were then plotted on to the cladogram and evidence was sought of disjunctions between the overall distribution of housekeeping genes and of connections between disparate clades, which could be taken as evidence of a recombinant event. The congruence of the gene phylogenies to each other and to the gltA phylogeny was tested by generating consensus (100 bootstraps) maximum likelihood phylogenies using PhyML ([20 (link)], performed on the Montpellier bioinformatics platform and the University of Oslo Bioportal), after first establishing optimal DNA evolution models for each gene using jModelTest [36 (link)]. The congruence between trees generated in this way for each gene and trees constrained by the assumption that the gltA phylogeny reflected the evolutionary history of the Bartonella isolates was tested using maximum likelihood ratio tests.
The unique sequences were deposited in GenBank under accession numbers GU338880-GU338885 (16S), GU338887-GU338901 (ftsZ), GU338903-GU338915 and GU338917-GU338924 (ribC), GU338925-GU338936 and GU338938-GU338941 (rpoB), GU338942-GU338976 (gltA), and GU559862-GU559871 and GU559873 (groEl).
Paziewska A., Harris P.D., Zwolińska L., Bajer A, & Siński E. (2010). Recombination Within and Between Species of the Alpha Proteobacterium Bartonella Infecting Rodents. Microbial Ecology, 61(1), 134-145.
Publication 2010
Alleles Bartonella Biological Evolution BP 100 Genes Genes, Housekeeping Genome Genotype Host Range Mutation Recombination, Genetic Trees

Most recents protocols related to «Host Range»

1
Genetic Manipulation of Pseudomonas putida
Plasmids and primers used in this study are listed in Tables S2 and S3. DNA manipulations were performed using standard methods [47 ].
KOD Hot Start DNA polymerase (Merck) or Phusion High-Fidelity DNA polymerase (New England Biolabs) were used for PCR reactions according to the manufacturer’s instructions. Primers were synthesized by Sigma-Aldrich and restriction enzymes were purchased from New England Biolabs. All DNA constructs were sequenced and verified to be correct before use.
The 343, 507 and 456 bp DNA regions containing the putative K1-T6SS promoter regions PtagB1, Phcp1 and PvrgG1, respectively, were amplified from genomic DNA extracted from the P. putida KT2440 strain using primers P1–P6 (Table S3). Promoters were cloned into the broad-host-range, low-copy-number vector pMP220 at the EcoRI/KpnI/PstI sites to produce transcriptional fusions to the lacZ gene. Recombinant plasmids were sequenced and transferred to P. putida by electroporation [48 (link)].
A DNA region upstream of PP3084 (173 bp) was amplified from genomic DNA extracted from the P. putida KT2440 strain using primers P7–P8 (Table S3) and cloned into pMP220 at EcoRI/XbaI sites to produce a transcriptional fusion to the lacZ gene. The gene PP3086 and its promoter region (711 bp) were amplified using primers P9–P10 (Table S3) and cloned into pMMB67EH at the EcoRI/HindIII sites. Recombinant plasmids (pMP220-based and pMMB67_PP3086) were sequenced and co-transferred to P. putida by electroporation [48 (link)].
Bernal P., Civantos C., Pacheco-Sánchez D., Quesada J.M., Filloux A, & Llamas M.A. (2023). Transcriptional organization and regulation of the Pseudomonas putida K1 type VI secretion system gene cluster. Microbiology, 169(1), 001295.
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Publication 2023
Cloning Vectors Deoxyribonuclease EcoRI DNA, A-Form DNA-Directed DNA Polymerase DNA Restriction Enzymes Electroporation Gene Fusion Genes Genome Host Range LacZ Genes Oligonucleotide Primers Plasmids Recombinant DNA Strains Transcription, Genetic
2
Phylogenetic Diversity of Ca. Hepatincolaceae
To investigate the diversity, host range and environmental distribution of taxa belonging to the ‘Ca. Hepatincolaceae’ family, we downloaded all 47 16S rRNA gene sequences assigned to this family from the SILVA database version SSU r138.1 [76 (link)] (https://www.arb-silva.de/). The sequences were downloaded as ARB alignment and duplicates were removed. The 16S rRNA gene sequences of the three Hepatincola strains were manually added to the alignment, as were seven representatives of the Caedimonadaceae and Paracaedibacteraceae as outgroup. All-gap sites were removed manually. A Maximum Likelihood phylogenetic tree based on the GTR + F + R3 model was produced using IQ-TREE v1.6.1 [73 (link)] with 1000 bootstrap replicates for internal branch support. The tree was visualized in iTOL v6.5.8 [77 (link)].
Dittmer J., Bredon M., Moumen B., Raimond M., Grève P, & Bouchon D. (2023). The terrestrial isopod symbiont ‘Candidatus Hepatincola porcellionum’ is a potential nutrient scavenger related to Holosporales symbionts of protists. ISME Communications, 3, 18.
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Publication 2023
Genes Host Range RNA, Ribosomal, 16S Strains Trees
3
Pluralistic Perspectives on Biosafety Norms
The value of biosafety has shown many different meanings throughout the course of our investigation. This plurality soon became apparent from the norms ascribed by our participants to biosafety. In Table 3 we present our findings of four main norms of biosafety: (I) Compliance with regulation; (II) Evaluation of microbes; (III) Responsibility; and, IV) Assessment of risk and uncertainty.

Main norm groups representing the different stakeholders’ views of the value of biosafety together with the corresponding design approaches. Each group is accompanied by a representative quote and sources supporting or stating each of the design approaches are shown in the last column with a numerical code (I = industry, A = academic, R = regulation, policy or technology transfer representative)

NormsDesign approachDescriptionSource
Compliance with regulation:“The physical containment is sufficient to comply with regulations, and that is the key thing, we need to comply with regulations.” (I2)Biological containmentUse of hosts microorganisms with a reduce host range, with natural or genetically modified characteristics that diminish their invading capacity or virulence, self-inactivating vectors, etc.I3, I4, I6, R1, A4
Physical containmentAll the physical barriers that prevent or minimize the escape of the microorganisms from the controlled settingsI2, I3, I6, R2, A4, I8
GMO-free productsSeparation of producer and products and inactivation of the biomassI3, I4, I5, A2, R1, I8
Historical argument of biosafetyEngineered strains retain the biosafety category granted to their ancestorsI2, I7, A3, I8, I9
RegulationBiosafety committees that take care of specific controls and standards. Additional approvals and bigger dossiers than other bioprocessesI2, I5, I6, I7, A3, R1, R2, I8, I9, R4
Evaluation of microbes:“The whole thing is that you should really study your microorganisms carefully and monitor things and be aware of what could happen.” (A4)Study of introduced genetic elementsMonitorization of stability and mobility of introduced genetic elementsI3, I9
SequencingSequence check of plasmids and full genomeI4, I5, I6, I7, I9
Other assaysGrowth, productivity and fitness assaysI5, I7, I8, I9
Responsibility:“That's why the Safe-by-design concept try to promote a proactive approach by actors so that the government doesn't need to solve problems afterwards, but that the actors who develop something, who innovate, who develop a new technology or new application, think about the safety aspects during that process.” (R2)Multi-actor responsibilityProactive responsibility at all stages of the process. Safe-by-design frameworkI4, R2, R3
Cellular barcodingAccountability through identification of labelled cells through space, time and even cell division which allows the instant access to all the information associated to a particular construct including its origin, its nature, if it is sensitive to antibiotics, what countermeasures one could take, etc.A3, I9
Assessment of risk and uncertainty:“If we work in a reasonably well-established organism like E. coli or Saccharomyces, I would say that we do not do any test to see whether it's safe or not. We basically go off the literature where others have done those tests already.” (I8)DomesticationHuman selection of strains to obtain cultivated variants that thrive in artificial niches and meet specific requirements. During this process, microbes become more efficient in consuming particular nutrients, coping with research- or industry-specific stress factors, and producing the target compounds, but this usually comes at the cost of a dramatic decrease of fitness in their natural environmentI4, I6, I9, R1
Scientific uncertaintyUncertain risks beyond the imposed norms and extra measuresI2, I4, I6, A3, A4, R2, I8, R3
Non-fitting assessmentCurrent regulation does not cover all the aspects of the technologyA2, R2
The norms collected in Table 3 reflect the plurality of the different understandings of biosafety encompassing categories that sometimes do not even vary along the same underlying dimensions (some of them are rules, some are individual practices, and some are general assumptions). While most of them could be considered complementary and particular facets of the value, some of them appear contradicting, which originates the possibility of value tensions (e.g., historical argument of biosafety vs. scientific uncertainty).
Genetic safeguards are not found in our empirical investigation. However, one could envision genetic safeguards as part of the biological containment (norm of compliance), or as part of multi-actor responsibility by giving deliberate attention to biosafety since the conception of a strain’s engineering (norms of responsibility).
Asin-Garcia E., Robaey Z., Kampers L.F, & Martins dos Santos V.A. (2023). Exploring the Impact of Tensions in Stakeholder Norms on Designing for Value Change: The Case of Biosafety in Industrial Biotechnology. Science and Engineering Ethics, 29(2), 9.
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Publication 2023
Antibiotics, Antitubercular Attention Biopharmaceuticals Cells Cloning Vectors Conception Division, Cell Escherichia coli Health Risk Assessment Host Range Nutrients Plasmids Range of Motion, Articular Reproduction Saccharomyces Safety Strains Virulence
4
Engineered E. coli for Diverse PHA Production
Four PhaC amino acid sequences were chosen based on the BLASTP search results. These phaC genes were chemically synthesized with optimized codon usage in E. coli by Eurofins Genomics Co. Ltd. (Tokyo, Japan) for plasmid construction and evaluation. E. coli LSBJ, a fadB fadJ double-deletion strain of E. coli LS5218 [fadR601, atoC (Con)] (Tappel et al., 2012a (link)), was used as the host strain for PHA biosynthesis. This strain is an ideal host for non-native PHA production because of its ability to take up a wide variety of substrates to be incorporated into PHA homo- and copolymers, and bench-level scale-up methodologies available for overall production (Tappel et al., 2012b (link); Levine et al., 2016 (link); Pinto et al., 2016 (link); Fadzil et al., 2018 (link); Furutate et al., 2021 (link); Scheel et al., 2021 (link)). A broad-host-range plasmid pBBR1MCS-2 (Kovach et al., 1995 (link)) harboring the genes encoding the PhaCs to be evaluated, the lac promoter region, the (R)-specific enoyl-CoA hydratase gene from A. caviae (phaJAc), the 3-ketothiolase gene (phaA) from Ralstonia eutropha H16, and the acetoacetyl-CoA reductase gene (phaB) from R. eutropha H16, termed pBBR1-phaCsABReJAc, was used for the expression of PhaCs (Supplementary Figure S1). For phaAB expression, the R. eutropha pha promoter and terminator regions were located upstream and downstream of their genes, respectively. To enhance the supply of 3HHx, 3H4MV, and 3H2MB monomers, the plasmid pTTQ-PCT (Furutate et al., 2017 (link)) containing the propionyl-CoA transferase (PCT) gene from Megasphaera elsdenii (pct) (Taguchi et al., 2008 (link)) was introduced into the E. coli LSBJ strain (Supplementary Figure S1).
Sivashankari R.M., Mierzati M., Miyahara Y., Mizuno S., Nomura C.T., Taguchi S., Abe H, & Tsuge T. (2023). Exploring Class I polyhydroxyalkanoate synthases with broad substrate specificity for polymerization of structurally diverse monomer units. Frontiers in Bioengineering and Biotechnology, 11, 1114946.
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Publication 2023
acetoacetyl-CoA reductase Acetyl-CoA C-Acyltransferase Amino Acid Sequence Anabolism Codon Usage Coenzyme A-Transferases Cupriavidus necator Deletion Mutation Escherichia coli Genes Homo Host Range Megasphaera elsdenii Plasmids propionyl-coenzyme A R-specific trans-2,3-enoylacyl-CoA hydratase Strains Terminator Regions, Genetic Transferase
5
Cucumber Root-Knot Nematode Infection
Cucumber (Cucumis sativus L.) cultivar “Xintaimici” was used in this study. Seeds were surface sterilized using 4% sodium hypochlorite and germinated on moistened filter paper in darkness for the RHI and the PCI methods. For the SHI method, seeds were surface sterilized and germinated on MS solid medium. M. incognita race 2 was maintained on cucumber in sterilized soil. The egg masses were collected and sterilized with 0.5% sodium hypochlorite for 3 min and then submerged in sterile water at 25 ℃ for 3 days. Freshly hatched pre-J2s were collected using a 500-mesh screen and stored in 4 ℃ before infection.
R. rhizogenes strain K599 (Weidi Biotechnology, China) was used in this study to induce hairy roots which harboring plasmid pRi2659 (agropine type) and had a wide range of hosts including Cucurbitaceae.
Zhang X., Li S., Li X., Song M., Ma S., Tian Y, & Gao L. (2023). Peat-based hairy root transformation using Rhizobium rhizogenes as a rapid and efficient tool for easily exploring potential genes related to root-knot nematode parasitism and host response. Plant Methods, 19, 22.
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Publication 2023
agropine Cucumis Cucumis sativus Cucurbitaceae Darkness Hair Host Range Infection Plant Embryos Plant Roots Plasmids Sodium Hypochlorite Sterility, Reproductive Strains

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1
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More about "Host Range"

Host range, also known as host specificity or host tropism, refers to the set of organisms that a biological entity, such as a virus, bacteria, or parasite, is capable of infecting or colonizing.
This concept is crucial in the fields of epidemiology, disease prevention, and targeted interventions.
Understanding the host range of a pathogen or organism is essential for researchers and healthcare professionals.
It helps identify potential reservoirs of infection, predict disease transmission patterns, and develop more effective diagnostic tools and treatments.
In the lab, techniques like M17 broth, MacConkey agar, and Dynabeads can be used to culture and identify microorganisms with specific host ranges.
Genetic engineering approaches, such as using the pGEM-T Easy vector and performing gene transformation in TOP10 cells with a Gene Pulser, can also shed light on the host range capabilities of different organisms.
Leveraging the power of AI-driven platforms like PubCompare.ai can further enhance the accuracy and reproducibility of host range research.
These tools can help researchers quickly identify and compare relevant information from published literature, preprints, and patents, minimizing errors and optimizing their workflows.
By understanding the host range of pathogens and other organisms, scientists can develop more targeted and effective interventions, ultimately improving public health and disease management.
This knowledge is also crucial for understanding the evolutionary dynamics and ecological interactions of different species.