Vibrio cholerae O1 associated with recent endemic cholera shows temporal changes in serotype, genotype, and drug-resistance patterns in Bangladesh.

BACKGROUND: Despite the advancement in our understanding of cholera and its etiological agent, Vibrio cholerae, the prevention and treatment of the disease are often hindered due to rapid changes in drug response pattern, serotype, and the major genomic islands namely, the CTX-prophage, and related genetic characteristics. In the present study, V. cholerae (n = 172) associated with endemic cholera in Dhaka during the years 2015-2021 were analyzed for major phenotypic and genetic characteristics, including drug resistance patterns. RESULTS: Results revealed that the V. cholerae strains belonged to serogroup O1 biotype El Tor carrying El Tor -specific genes rtxC, tcpA El Tor, and hlyA El Tor, but possessed classical-biotype cholera toxin. Serotypes of V. cholerae strains differed temporally in predominance with Inaba during 2015-2017, and again in 2020-2021, while Ogawa was the predominant serotype in 2018-2019. Also, ctxB1 was predominant in V. cholerae associated with cholera during 2015-2017, while ctxB7 was predominant in 2018, and in the subsequent years, as observed until 2021. V. cholerae strains differed in their antibiotic resistance pattern with a majority (97%) being multi-drug resistant (MDR) and belonging to six sub-groups. Notably, one of these MDR strains was resistant to eleven of the eighteen antibiotics tested, with resistance to fourth-generation cephalosporin (cefepime), and aztreonam. This extreme drug resistant (XDR) strain carried resistance-related genes namely, extended-spectrum ß-lactamases (ESBL), blaOXA-1 and blaPER-3. CONCLUSION: The observed temporal switching of serotypes, as well as the ctxB genotype, and the emergence of MDR/XDR V. cholerae and their association with endemic cholera in Dhaka underscore the need for routine monitoring of the pathogen for proper patient management.

Genomic attributes of Vibrio cholerae O1 responsible for 2022 massive cholera outbreak in Bangladesh.

In 2022, one of its worst cholera outbreaks began in Bangladesh and the icddr,b Dhaka hospital treated more than 1300 patients and ca. 42,000 diarrheal cases from March-1 to April-10, 20221. Here, we present genomic attributes of V. cholerae O1 responsible for the 2022 Dhaka outbreak and 960 7th pandemic El Tor (7PET) strains from 88 countries. Results show strains isolated during the Dhaka outbreak cluster with 7PET wave-3 global clade strains, but comprise subclade BD-1.2, for which the most recent common ancestor appears to be that responsible for recent endemic cholera in India. BD-1.2 strains are present in Bangladesh since 2016, but not establishing dominance over BD-2 lineage strains2 until 2018 and predominantly associated with endemic cholera. In conclusion, the recent shift in lineage and genetic attributes, including serotype switching of BD-1.2 from Ogawa to Inaba, may explain the increasing number of cholera cases in Bangladesh.

Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits.

BACKGROUND: Maize (Zea Mays) is one of the world's most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties. RESULT: In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21-34 highly significant SNPs (-log10P > 5), with an estimated total heritability of 37.31-62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ([Formula: see text] 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS. CONCLUSIONS: Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.

Genomic Characteristics of Recently Recognized Vibrio cholerae El Tor Lineages Associated with Cholera in Bangladesh, 1991 to 2017.

Comparative genomic analysis of Vibrio cholerae El Tor associated with endemic cholera in Asia revealed two distinct lineages, one dominant in Bangladesh and the other in India. An in-depth whole-genome study of V. cholerae El Tor strains isolated during endemic cholera in Bangladesh (1991 to 2017) included reference genome sequence data obtained online. Core genome phylogeny established using single nucleotide polymorphisms (SNPs) showed V. cholerae El Tor strains comprised two lineages, BD-1 and BD-2, which, according to Bayesian phylodynamic analysis, originated from paraphyletic group BD-0 around 1981. BD-1 and BD-2 lineages overlapped temporally but were negatively associated as causative agents of cholera during 2004 to 2017. Genome-wide association study (GWAS) revealed 140 SNPs and 31 indels, resulting in gene alleles unique to BD-1 and BD-2. Regression analysis of root to tip distance and year of isolation indicated early BD-0 strains at the base, whereas BD-1 and BD-2 subsequently emerged and progressed by accumulating SNPs. Pangenome analysis provided evidence of gene acquisition by both BD-1 and BD-2, of which six crucial proteins of known function were predominant in BD-2. BD-1 and BD-2 diverged and have distinctively different genomic traits, namely, heterogeneity in VSP-2, VPI-1, mobile elements, toxin encoding elements, and total gene abundance. In addition, the observed phage-inducible chromosomal island-like element (PLE1), and SXT ICE elements (ICETET) in BD-2 presumably provided a fitness advantage for the lineage to outcompete BD-1 as the etiological agent of endemic cholera in Bangladesh, with implications for global cholera epidemiology. IMPORTANCE Cholera is a global disease with specific reference to the Bay of Bengal Ganges Delta where Vibrio cholerae O1 El Tor, the causative agent of the disease showed two circulating lineages, one dominant in Bangladesh and the other in India. Results of an in-depth genomic study of V. cholerae associated with endemic cholera during the past 27 years (1991 to 2017) indicate emergence and succession of the two lineages, BD-1 and BD-2, arising from a common ancestral paraphyletic group, BD-0, comprising the early strains and short-term evolution of the bacterium in Bangladesh. Among the two V. cholerae lineages, BD-2 supersedes BD-1 and is predominant in the most recent endemic cholera in Bangladesh. The BD-2 lineage contained significantly more SNPs and indels, and showed richness in gene abundance, including antimicrobial resistance genes, gene cassettes, and PLE to fight against bacteriophage infection, acquired over time. These findings have important epidemic implications on a global scale.

Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population.

Body surface area (BSA) is an important trait used for many clinical purposes. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10-5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ -0.013 for T/T and 0.013 for G/G, -Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and -0.012 for G/G, -Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ -0.018 for G/G and 0.018 for C/C, -Log10 PEW = 19.737) and dominance (d≙ -0.038 for G/C, -Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

Conditional and unconditional genome-wide association study reveal complicate genetic architecture of human body weight and impacts of smoking.

To reveal the impacts of smoking on genetic architecture of human body weight, we conducted a genome-wide association study on 5,336 subjects in four ethnic populations from MESA (The Multi-Ethnic Study of Atherosclerosis) data. A full genetic model was applied to association mapping for analyzing genetic effects of additive, dominance, epistasis, and their ethnicity-specific effects. Both the unconditional model (base) and conditional model including smoking as a cofactor were investigated. There were 10 SNPs involved in 96 significant genetic effects detected by the base model, which accounted for a high heritability (61.78%). Gene ontology analysis revealed that a number of genetic factors are related to the metabolic pathway of benzopyrene, a main compound in cigarettes. Smoking may play important roles in genetic effects of dominance, dominance-related epistasis, and gene-ethnicity interactions on human body weight. Gene effect prediction shows that the genetic effects of smoking cessation on body weight vary from different populations.

ß-composite Interval Mapping for robust QTL analysis.

Interval mapping approaches have been playing significant role for quantitative trait locus (QTL) mapping to discover genetic architecture of diseases or traits with molecular markers. Composite interval mapping (CIM) is one of the superior approaches of the interval mapping for discovering both linked and unlinked putative QTL positions. However, estimators of this approach are not robust against phenotypic outliers. As a result, it fails to detect true QTL positions in presence of outliers. In this study, we investigated the performance of ß-Composite Interval Mapping (BetaCIM) for detecting both linked and unlinked important QTLs positions from the robustness points of views. Performance of this approach depends on the value of tuning parameter ß. It reduces to the classical CIM approach for ß â†’0. We described and formulated the cross-validation procedure for selecting trait specific optimum value of ß. It was observed that the optimum value of ß depends on both amount of contaminated observations and their scatteredness. BetaCIM approach discover similar QTL positions as classical IM/CIM in absence of phenotypic outliers, but gives better results in presence of phenotypic outliers in terms of detecting true QTLs and effects estimation. We formulated the generalized forms of robust QTL analysis and developed an R-package named "BetaCIM" by implementing this robust approach. Left and right kidney weight data sets of mouse intercross population (129 S1/SvlmJ × A/J) were analyzed by using BetaCIM, CIM, and IM approaches. For right kidney weight (RKW) CIM and BetaCIM provided similar LOD score profile, and both approaches identified 3 QTL positions. IM approach also identified 3 QTL positions. For left kidney weight (LKW), there was evidence of one outlying observation; and in this case the BetaCIM approach identified 2 QTL positions. However, none of the QTLs were significant by CIM and IM approaches at 5% level of significance. Gene expression ontology (GEO) search showed that the candidate genes (Otof and A330033J07Rik) of the identified QTLs for LKW were expressed in kidney. Both simulation and real data analysis results showed that BetaCIM approach improves the performance over the existing methods in presence of phenotypic outliers. Otherwise, it keeps almost equal performance.

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture.

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits.