scholarly journals Interaction analysis of Mycobacterium tuberculosis between host environment and highly mutated genes from population genetic structure comparison

2020 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Yue Chang ◽  
Jing Ou ◽  
Liwen Huang
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Interaction Analysis ◽  
Hot Spot ◽  
Genetic Mutation ◽  
Independent Effect ◽  
Multivariate Logistic Regression Model ◽  
Cold Spot ◽  
Structure Comparison ◽  
Significant Difference

Abstract Objective We aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. Tb) are distributed uniformly in cold and hot spots. Methods The cold and hot spot areas were identified using the reported incidence of TB over the previous 5 years. Whole genome sequencing was performed on 291 M. tb isolates between January and June 2018. Analysis of molecular variance (AMOVA) and a multifactor dimensionality reduction (MDR) model was applied to test gene-gene-environment interactions. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were computed to test the extent to which genetic mutation affects the TB epidemic using a multivariate logistic regression model. Results The percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, p = 0.022), among elderly, people with a low BMI and those having a history of contact with a TB patient (all p < 0.05). Individuals from cold spot areas had a higher frequency of out-of-town travelling (p < 0.05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210 and a Intergenic Region (SNP site: 3847237) showed a significant difference between cold and hot spots. (p < 0.001). The MDR model displayed a clear negative interaction effect of age groups with BMI (interaction entropy: -3.55%) and mutation of Rv0210 (interaction entropy: -2.39%). Through the mutations of Rv0210 and BMI had a low independent effect (interaction entropy: -1.46%). Conclusion Our data suggests a statistical significant role of age, BMI and the polymorphisms of Rv0210 genes in the transmission and development of M. tb. The results provide clues for the study of susceptibility genes of M. tb in different population.

scholarly journals Interaction analysis of Mycobacterium tuberculosis between host environment and highly mutated genes from population genetic structure comparison

2020 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Yue Chang ◽  
Jing Ou ◽  
Liwen Huang
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Low Income ◽  
Hot Spots ◽  
Interaction Analysis ◽  
Hot Spot ◽  
Minority Group ◽  
Independent Effect ◽  
Cold Spot ◽  
Structure Comparison ◽  
Significant Difference

Abstract ObjectiveWe aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. Tb) are distributed uniformly in cold and hot spots. MethodsThe cold and hot spot areas were identified using the reported incidence of TB over the previous 5 years. Whole genome sequencing was performed on 291 M. tb isolates between January and June 2018. Analysis of molecular variance (AMOVA) and a multifactor dimensionality reduction (MDR) model was applied to test gene-gene-environment interactions. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were computed to test the extent to which genetic mutation affects the TB epidemic using a multivariate logistic regression model. ResultsThe percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, p = 0.022), among elderly, people from an ethnic minority group, those with a low income, low BMI and those having a history of contact with a TB patient (all p < 0.05).. Individuals from cold spot areas had a higher frequency of out-of-town travelling (p < 0.05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210 and a Intergenic Region (SNP site: 3847237) showed a significant difference between cold and hot spots. (p < 0.001). The best MDR model displayed a clear negative interaction effect of ethnicity with income (interaction entropy: -7.08%), mutation of Rv1186c (interaction entropy: -5.62%) and mutation of SNP position Rv0210 (interaction entropy: -4.26%). Through income, mutation of Rv1186c and mutation of Rv0210 had a low independent effect.ConclusionOur data suggests a statistically significant role of ethnicity, income and the polymorphisms of Rv1186c and Rv0210 genes in the transmission and development of M. tb. The results provide clues for the study of susceptibility genes of M. tb in different ethnic groups.

scholarly journals The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

2020 ◽  
Author(s):  
Dingwen Lin ◽  
Zhezhe Cui ◽  
Virasakdi Virasakdi ◽  
Prasit Palittapongarnpim ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Sequencing Analysis ◽  
Spot Area ◽  
H37rv Strain ◽  
Lineage 2 ◽  
Cold Spots ◽  
Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. Objective This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots.Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

scholarly journals The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

2020 ◽  
Author(s):  
Dingwen Lin ◽  
Zhezhe Cui ◽  
Virasakdi Virasakdi ◽  
Prasit Palittapongarnpim ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Sequencing Analysis ◽  
Spot Area ◽  
H37rv Strain ◽  
Lineage 2 ◽  
Cold Spots ◽  
Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

scholarly journals Hot and Cold Spot Areas of Household Tuberculosis Transmission in Southern China: Effects of Socio-Economic Status and Mycobacterium tuberculosis Genotypes

2019 ◽  
Vol 16 (10) ◽  
pp. 1863 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Virasakdi Chongsuvivatwong ◽  
Edward A. Graviss ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Socioeconomic Factor ◽  
Skin Tests ◽  
P Value ◽  
Cold Spot ◽  
Medical Facility ◽  
Beijing Family ◽  
Household Contacts

The aims of the study were: (1) compare sociodemographic characteristics among active tuberculosis (TB) cases and their household contacts in cold and hot spot transmission areas, and (2) quantify the influence of locality, genotype and potential determinants on the rates of latent tuberculosis infection (LTBI) among household contacts of index TB cases. Parallel case-contact studies were conducted in two geographic areas classified as “cold” and “hot” spots based on TB notification and spatial clustering between January and June 2018 in Guangxi, China, using data from field contact investigations, whole genome sequencing, tuberculin skin tests (TSTs), and chest radiographs. Beijing family strains accounted for 64.6% of Mycobacterium tuberculosis (Mtb) strains transmitted in hot spots, and 50.7% in cold spots (p-value = 0.02). The positive TST rate in hot spot areas was significantly higher than that observed in cold spot areas (p-value < 0.01). Living in hot spots (adjusted odds ratio (aOR) = 1.75, 95%, confidence interval (CI): 1.22, 2.50), Beijing family genotype (aOR = 1.83, 95% CI: 1.19, 2.81), living in the same room with an index case (aOR = 2.29, 95% CI: 1.5, 3.49), travelling time from home to a medical facility (aOR = 4.78, 95% CI: 2.96, 7.72), history of Bacillus Calmette-Guérin vaccination (aOR = 2.02, 95% CI: 1.13 3.62), and delay in diagnosis (aOR = 2.56, 95% CI: 1.13, 5.80) were significantly associated with positive TST results among household contacts of TB cases. The findings of this study confirmed the strong transmissibility of the Beijing genotype family strains and this genotype’s important role in household transmission. We found that an extended traveling time from home to the medical facility was an important socioeconomic factor for Mtb transmission in the family. It is still necessary to improve the medical facility infrastructure and management, especially in areas with a high TB prevalence.

scholarly journals The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

2020 ◽  
Author(s):  
Dingwen Lin ◽  
Zhezhe Cui ◽  
Virasakdi Virasakdi ◽  
Prasit Palittapongarnpim ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Sequencing Analysis ◽  
Spot Area ◽  
H37rv Strain ◽  
Lineage 2 ◽  
Cold Spots ◽  
Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

scholarly journals Identifying diabetes management opportunity areas in the USA

2020 ◽  
Vol 8 (1) ◽  
pp. e000293 ◽  
Author(s):  
Michael Topmiller ◽  
Kyle Shaak ◽  
Peter J Mallow ◽  
Autumn M Kieber-Emmons
Keyword(s):  
Hot Spots ◽  
Diabetes Management ◽  
Hot Spot ◽  
The United States ◽  
Cold Spot ◽  
United States Census ◽  
Preventable Hospitalisation ◽  
Cold Spots ◽  
Medicare Spending ◽  
Management Services

Using adherence to diabetes management guidelines as a case study, this paper applied a novel geospatial hot-spot and cold-spot methodology to identify priority counties to target interventions. Data for this study were obtained from the Dartmouth Atlas of Healthcare, the United States Census Bureau’s American Community Survey and the University of Wisconsin County Health Rankings. A geospatial approach was used to identify four tiers of priority counties for diabetes preventive and management services: diabetes management cold-spots, clusters of counties with low rates of adherence to diabetes preventive and management services (Tier D); Medicare spending hot-spots, clusters of counties with high rates of spending and were diabetes management cold-spots (Tier C); preventable hospitalisation hot-spots, clusters of counties with high rates of spending and are diabetes management cold-spots (Tier B); and counties that were located in a diabetes management cold-spot cluster, preventable hospitalisation hot-spot cluster and Medicare spending hot-spot cluster (Tier A). The four tiers of priority counties were geographically concentrated in Texas and Oklahoma, the Southeast and central Appalachia. Of these tiers, there were 62 Tier A counties. Rates of preventable hospitalisations and Medicare spending were higher in Tier A counties compared with national averages. These same counties had much lower rates of adherence to diabetes preventive and management services. The novel geospatial mapping approach used in this study may allow practitioners and policy makers to target interventions in areas that have the highest need. Further refinement of this approach is necessary before making policy recommendations.

scholarly journals Geospatial cluster analyses of pneumonia-associated hospitalisations among adults in New York City, 2010–2014

2018 ◽  
Vol 147 ◽  
Author(s):  
P. A. Kache ◽  
T. Julien ◽  
R. E. Corrado ◽  
N. M. Vora ◽  
D. C. Daskalakis ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Hot Spots ◽  
Hot Spot ◽  
Severe Pneumonia ◽  
Cold Spot ◽  
Staten Island ◽  
Cluster Analyses ◽  
Cold Spots

AbstractPneumonia is a leading cause of death in New York City (NYC). We identified spatial clusters of pneumonia-associated hospitalisation for persons residing in NYC, aged ⩾18 years during 2010–2014. We detected pneumonia-associated hospitalisations using an all-payer inpatient dataset. Using geostatistical semivariogram modelling, local Moran'sIcluster analyses andχ2tests, we characterised differences between ‘hot spots’ and ‘cold spots’ for pneumonia-associated hospitalisations. During 2010–2014, there were 141 730 pneumonia-associated hospitalisations across 188 NYC neighbourhoods, of which 43.5% (N= 61 712) were sub-classified as severe. Hot spots of pneumonia-associated hospitalisation spanned 26 neighbourhoods in the Bronx, Manhattan and Staten Island, whereas cold spots were found in lower Manhattan and northeastern Queens. We identified hot spots of severe pneumonia-associated hospitalisation in the northern Bronx and the northern tip of Staten Island. For severe pneumonia-associated hospitalisations, hot-spot patients were of lower mean age and a greater proportion identified as non-Hispanic Black compared with cold spot patients; additionally, hot-spot patients had a longer hospital stay and a greater proportion experienced in-hospital death compared with cold-spot patients. Pneumonia prevention efforts within NYC should consider examining the reasons for higher rates in hot-spot neighbourhoods, and focus interventions towards the Bronx, northern Manhattan and Staten Island.

scholarly journals SPATIO-TEMPORAL CHANGES IN WHEAT CROP CULTIVATION IN INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 385-395
Author(s):  
P. Krishnan ◽  
P. Aggarwal ◽  
N. Mridha ◽  
V. Bajpai
Keyword(s):  
Hot Spots ◽  
Crop Production ◽  
Hot Spot ◽  
Space Time ◽  
Background Information ◽  
Wheat Crop ◽  
Cold Spot ◽  
Indian States ◽  
Crop Cultivation ◽  
Production Area

<p><strong>Abstract.</strong> This study was conducted to understand the changes in spatiotemporal characteristics of wheat crop production including the changes in area and yield. We employed the emerging hot and cold spot analysis along with space time cube and space-time cluster density analysis to study the spatial changes in wheat crop production, area and yield, and understand the changes in spatiotemporal features. We made a comprehensive analysis of the changes in wheat crop production, area and yield on pan India basis for the period from 1999 to 2015. The major findings were: (a) During the study period significant increase in wheat yield occurred within the North Indian states of Punjab and Haryana and intensifying hot spots appeared within the Indo-Gangetic plains. (b) The Analysis of the area under wheat cultivation showed a persistent hot spot in the Northern states of Uttarakhand and Uttar Pradesh, Punjab and Haryana, with new hot spots observed in the regions of Central India during the years 2014 and 2015. (d) The analysis of the wheat crop production showed significant new cold spots in Rajasthan and Gujarat, with intensifying hotspots emanating into the lower delta regions of Ganges. Present study also revealed the potential of GIS based data models when related with additional background information, to segregate the most significant clusters of changes (increase / decrease) happening over active wheat crop cultivation. We expect the results from this study to help in increasing the wheat crop yield and production in the future.</p>

Geographic distribution of livestock products in China- an application of spatial autocorrelation analysis

2016 ◽  
Author(s):  
Ling Gan ◽  
Xisheng HU
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Geographic Distribution ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Northern Coast ◽  
Livestock Industry ◽  
North Eastern ◽  
Livestock Products

The rapid increase in the livestock industry in China in recent two decades has played an important role in the livelihoods of people and has become a very significant issue in terms of sustainable animal food supply chains. Knowledge gaps in the geographic distribution may hinder the sustainable development of livestock industry. This paper investigates the spatial distribution in the outputs of livestock products (meat, milk and egg, respectively) in China using exploratory spatial data analysis. This method is a set of GIS spatial statistical techniques that are useful in describing and visualizing the spatial distribution, detecting patterns of hot-spots, and suggesting spatial regimes. The global Moran’s I statistics for the three products reveal strong positive and significant spatial autocorrelation. Furthermore, the Moran significance maps indicate four hot-spots (North-eastern cluster, Northern Coast cluster, Central Inland cluster and Southern cluster) and one cold-spot (Western cluster) in the meat product distribution, one large hot-spot (Northern cluster) and one large cold-spot (South-central cluster) for the milk product, four relatively small hot-spots (North-eastern cluster, Northern Coast cluster, Eastern Coast cluster and Central Inland cluster) and one large cold-spot (Western cluster) for the egg product. Based on the results, we show that livestock products are polarized into clusters and the outputs of the products tend to be reducing from east to west and from north to south China. Implications are drawn, such as priority of resource allocations for hot-spot area in terms of animal-source food security and the utilization of spillover effects from hot-spots.

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