Successful MDR-TB Treatment Regimens Including Amikacin are Associated with High Rates of Hearing Loss

Aminoglycosides are a critical component of multidrug-resistant tuberculosis (MDR-TB) treatment but data on their efficacy and adverse effects in Botswana is scarce. We determined the effect of amikacin on treatment outcomes and development of hearing loss in MDR-TB patients. Patients started on MDR-TB treatment between 2006 and 2012 were included. Multivariate analysis was used to determine the effect of amikacin on treatment outcomes and development of hearing loss

Expression of Complement and Toll-Like Receptor Pathway Genes is Associated with Malaria Severity in Mali: A Pilot Case Control Study

Background: The host response to infection by Plasmodium falciparum, the parasite most often responsible for severe malaria, ranges from asymptomatic parasitaemia to death. The clinical trajectory of malaria is influenced by host genetics and parasite load, but the factors determining why some infections produce uncomplicated malaria and some proceed to severe disease remain incompletely understood. Methods: To identify molecular markers of severe falciparum malaria, human gene expression patterns were compared between children aged 6 months to 5 years with severe and uncomplicated malaria who were enrolled in a case–control study in Bandiagara, Mali. Microarrays were used to obtain expression data on severe cases and uncomplicated controls at the time of acute disease presentation (five uncomplicated and five severe), 1 week after presentation (three uncomplicated and three severe) and treatment initiation, and in the subsequent dry season (late convalescence, four uncomplicated and four severe). This is a pilot study for the first use of microarray technology in Mali

Co-infection with HPV Types from the Same Species Provides Natural Cross-Protection from Progression to Cervical Cancer

The worldwide administration of bivalent and quadrivalent HPV vaccines has resulted in cross-protection against non-vaccine HPV types. Infection with multiple HPV types may offer similar cross-protection in the natural setting. We hypothesized that infections with two or more HPV types from the same species, and independently, infections with two or more HPV types from different species, associate with protection from high-grade lesions

Disrupted Human–Pathogen Co-Evolution: A Model for Disease

A major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable

Disrupted Human–Pathogen Co-Evolution: A Model for Disease

A major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable

Human and Helicobacter Pylori Coevolution Shapes the Risk of Gastric Disease

Helicobacter pylori is the principal cause of gastric cancer, the second leading cause of cancer mortality worldwide. However, H. pylori prevalence generally does not predict cancer incidence. To determine whether coevolution between host and pathogen influences disease risk, we examined the association between the severity of gastric lesions and patterns of genomic variation in matched human and H. pylori samples. Patients were recruited from two geographically distinct Colombian populations with significantly different incidences of gastric cancer, but virtually identical prevalence of H. pylori infection. All H. pylori isolates contained the genetic signatures of multiple ancestries, with an ancestral African cluster predominating in a low-risk, coastal population and a European cluster in a high-risk, mountain population. The human ancestry of the biopsied individuals also varied with geography, with mostly African ancestry in the coastal region (58%), and mostly Amerindian ancestry in the mountain region (67%). The interaction between the host and pathogen ancestries completely accounted for the difference in the severity of gastric lesions in the two regions of Colombia. In particular, African H. pylori ancestry was relatively benign in humans of African ancestry but was deleterious in individuals with substantial Amerindian ancestry. Thus, coevolution likely modulated disease risk, and the disruption of coevolved human and H. pylori genomes can explain the high incidence of gastric disease in the mountain population

Sex-Specific Parental Effects on Offspring Lipid Levels

Background: Plasma lipid levels are highly heritable traits, but known genetic loci can only explain a small portion of their heritability. Methods and Results: In this study, we analyzed the role of parental levels of total cholesterol (TC), low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), and triglycerides (TGs) in explaining the values of the corresponding traits in adult offspring. We also evaluated the contribution of nongenetic factors that influence lipid traits (age, body mass index, smoking, medications, and menopause) alone and in combination with variability at the genetic loci known to associate with TC, LDL‐C, HDL‐C, and TG levels. We performed comparisons among different sex‐specific regression models in 416 families from the Framingham Heart Study and 304 from the SardiNIA cohort. Models including parental lipid levels explain significantly more of the trait variation than models without these measures, explaining up to ≈39% of the total trait variation. Of this variation, the parent‐of‐origin effect explains as much as ≈15% and it does so in a sex‐specific way. This observation is not owing to shared environment, given that spouse‐pair correlations were negligible (\u3c1.5% explained variation in all cases) and is distinct from previous genetic and acquired factors that are known to influence serum lipid levels. Conclusions: These findings support the concept that unknown genetic and epigenetic contributors are responsible for most of the heritable component of the plasma lipid phenotype, and that, at present, the clinical utility of knowing age‐matched parental lipid levels in assessing risk of dyslipidemia supersedes individual locus effects. Our results support the clinical utility of knowing parental lipid levels in assessing future risk of dyslipidemia

Diverse Convergent Evidence in the Genetic Analysis of Complex Disease: Coordinating Omic, Informatic, and Experimental Evidence to Better Identify and Validate Risk Factors

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions

Genomics of human pulmonary tuberculosis: from genes to pathways

Tuberculosis (TB), caused by (MTB), remains a major public health threat globally. Several lines of evidence support a role for host genetic factors in resistance/susceptibility to TB disease and MTB infection. However, results across candidate gene and genome-wide association studies (GWAS) are largely inconsistent, so a cohesive genetic model underlying TB risk has not emerged. Despite the difficulties in identifying consistent genetic associations, genetic studies of TB and MTB infection have revealed a few well-documented loci. These well validated genes are presented in this review, but there remains a large gap in how these genes translate into better understanding of TB. To address this, we present a pathway based extension of standard association analyses, seeding the results with the best validated genes from candidate gene and GWAS studies. Several pathways were significantly enriched using pathway analyses that may help to explain population patterns of TB risk. In conclusion, we advocate for novel approaches to the study of host genetic analysis of TB that extend traditional association approaches