A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools

Polar Microalgae: New Approaches towards Understanding Adaptations to an Extreme and Changing Environment

Polar Regions are unique and highly prolific ecosystems characterized by extreme environmental gradients. Photosynthetic autotrophs, the base of the food web, have had to adapt physiological mechanisms to maintain growth, reproduction and metabolic activity despite environmental conditions that would shut-down cellular processes in most organisms. High latitudes are characterized by temperatures below the freezing point, complete darkness in winter and continuous light and high UV in the summer. Additionally, sea-ice, an ecological niche exploited by microbes during the long winter seasons when the ocean and land freezes over, is characterized by large salinity fluctuations, limited gas exchange, and highly oxic conditions. The last decade has been an exciting period of insights into the molecular mechanisms behind adaptation of microalgae to the cryosphere facilitated by the advancement of new scientific tools, particularly “omics” techniques. We review recent insights derived from genomics, transcriptomics, and proteomics studies. Genes, proteins and pathways identified from these highly adaptable polar microbes have far-reaching biotechnological applications. Furthermore, they may provide insights into life outside this planet, as well as glimpses into the past. High latitude regions also have disproportionately large inputs into global biogeochemical cycles and are the region most sensitive to climate change

Evidence For Gene-Smoking interactions For Hearing Loss and Deafness in Japanese american Families

BACKGROUND: This study investigated the relationship between smoking and hearing loss and deafness (HLD) and whether the relationship is modified by genetic variation. Data for these analyses was from the subset of Japanese American families collected as part of the American Diabetes Association Genetics of Non-insulin Dependent Diabetes Mellitus study. Logistic regression with generalized estimating equations assessed the relationship between HLD and smoking. Nonparametric linkage analysis identified genetic regions harboring HLD susceptibility genes and ordered subset analysis was used to identify regions showing evidence for gene-smoking interactions. Genetic variants within these candidate regions were then each tested for interaction with smoking using logistic regression models. RESULTS: After adjusting for age, sex, diabetes status and smoking duration, for each pack of cigarettes smoked per day, risk of HLD increased 4.58 times (odds ratio (OR) = 4.58; 95% Confidence Interval (CI): (1.40,15.03)), and ever smokers were over 5 times more likely than nonsmokers to report HLD (OR = 5.22; 95% CI: (1.24, 22.03)). Suggestive evidence for linkage for HLD was observed in multiple genomic regions (Chromosomes 5p15, 8p23 and 17q21), and additional suggestive regions were identified when considering interactions with smoking status (Chromosomes 7p21, 11q23, 12q32, 15q26, and 20q13) and packs-per-day (Chromosome 8q21). CONCLUSIONS: to our knowledge this was the first report of possible gene-by-smoking interactions in HLD using family data. Additional work, including independent replication, is needed to understand the basis of these findings. HLD are important public health issues and understanding the contributions of genetic and environmental factors may inform public health messages and policies

Protective variant associated with alcohol dependence in a Mexican American cohort

Abstract Background Mexican Americans, particularly those born in the United States, are at greater risk for alcohol associated morbidity and mortality. The present study sought to investigate whether specific genetic variants may be associated with alcohol use disorder phenotypes in a select population of Mexican American young adults. Methods The study evaluated a cohort of 427 (age 18 – 30 years) Mexican American men (n = 171) and women (n = 256). Information on alcohol dependence was obtained through interview using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA). For all subjects, DNA was extracted from blood samples, followed by genotyping using an Affymetrix Axiom Exome1A chip. Results A protective variant (rs991316) located downstream from the ADH7 (alcohol dehydrogenase 7) gene showed suggestive significance in association with alcohol dependence symptom counts derived from DSM-III-R and DSM-IV criteria, as well as to clustered alcohol dependence symptoms. Additional linkage analysis suggested that nearby variants in linkage disequilibrium with rs991316 were not responsible for the observed association with the alcohol dependence phenotypes in this study. Conclusions ADH7 has been shown to have a protective role against alcohol dependence in previous studies involving other ethnicities, but has not been reported for Mexican Americans. These results suggest that variants near ADH7 may play a role in protection from alcohol dependence in this Mexican American cohort

Rationale, Study Design, and Cohort Characteristics for the Markers for Environmental Exposures (MEE) Study.

Environmental factors have been linked to many diseases and health conditions, but reliable assessment of environmental exposures is challenging. Developing biomarkers of environmental exposures, rather than relying on self-report, will improve our ability to assess the association of such exposures with disease. Epigenetic markers, most notably DNA methylation, have been identified for some environmental exposures, but identification of markers for additional exposures is still needed. The rationale behind the Markers for Environmental Exposures (MEE) Study was to (1) identify biomarkers, especially epigenetic markers, of environmental exposures, such as pesticides, air/food/water contaminants, and industrial chemicals that are commonly encountered in the general population; and (2) support the study of potential relationships between environmental exposures and health and health-related factors. The MEE Study is a cross-sectional study with potential for record linkage and follow-up. The well-characterized cohort of 400 postmenopausal women has generated a repository of biospecimens, including blood, urine, and saliva samples. Paired data include an environmental exposures questionnaire, a breast health questionnaire, dietary recalls, and a food frequency questionnaire. This work describes the rationale, study design, and cohort characteristics of the MEE Study. In addition to our primary research goals, we hope that the data and biorepository generated by this study will serve as a resource for future studies and collaboration

Variants Near CCK Receptors are Associated With Electrophysiological Responses to Pre-pulse Startle Stimuli in a Mexican American Cohort

Neurophysiological measurements of the response to pre-pulse and startle stimuli have been suggested to represent an important endophenotype for both substance dependence and other select psychiatric disorders. We have previously shown, in young adult Mexican Americans (MA), that presentation of a short delay acoustic pre-pulse, prior to the startle stimuli can elicit a late negative component at about 400 msec (N4S), in the event-related potential (ERP), recorded from frontal cortical areas. In the present study, we investigated whether genetic factors associated with this endophenotype could be identified. The study included 420 (age 18–30 years) MA men ( n = 170), and women ( n = 250). DNA was genotyped using an Affymetrix Axiom Exome1A chip. An association analysis revealed that the CCKAR and CCKBR (cholecystokinin A and B receptor) genes each had a nearby variant that showed suggestive significance with the amplitude of the N4S component to pre-pulse stimuli. The neurotransmitter cholecystokinin ( CCK ), along with its receptors, CCKAR and CCKBR , have been previously associated with psychiatric disorders, suggesting that variants near these genes may play a role in the pre-pulse/startle response in this cohort

Correlation Analysis of Genetic Admixture and Social Identification with Body Mass Index in a Native American Community

OBJECTIVES: Body mass index (BMI) is a well-known measure of obesity with a multitude of genetic and non-genetic determinants. Identifying the underlying factors associated with BMI is difficult because of its multifactorial etiology that varies as a function of geoethnic background and socioeconomic setting. Thus, we pursued a study exploring the influence of the degree of Native American admixture on BMI (as well as weight and height individually) in a community sample of Native Americans (n = 846) while accommodating a variety of socioeconomic and cultural factors. METHODS: Participants' degree of Native American (NA) ancestry was estimated using a genome-wide panel of markers. The participants also completed an extensive survey of cultural and social identity measures: the Indian Culture Scale (ICS) and the Orthogonal Cultural Identification Scale (OCIS). Multiple linear regression was used to examine the relation between these measures and BMI. RESULTS: Our results suggest that BMI is correlated positively with the proportion of NA ancestry. Age was also significantly associated with BMI, while gender and socioeconomic measures (education and income) were not. For the two cultural identity measures, the ICS showed a positive correlation with BMI, while OCIS was not associated with BMI. CONCLUSIONS: Taken together, these results suggest that genetic and cultural environmental factors, rather than socioeconomic factors, account for a substantial proportion of variation in BMI in this population. Further, significant correlations between degree of NA ancestry and BMI suggest that admixture mapping may be appropriate to identify loci associated with BMI in this population

Variants Near CCK Receptors are Associated With Electrophysiological Responses to Pre-pulse Startle Stimuli in a Mexican American Cohort

Neurophysiological measurements of the response to pre-pulse and startle stimuli have been suggested to represent an important endophenotype for both substance dependence and other select psychiatric disorders. We have previously shown, in young adult Mexican Americans (MA), that presentation of a short delay acoustic pre-pulse, prior to the startle stimuli can elicit a late negative component at about 400 msec (N4S), in the event-related potential (ERP), recorded from frontal cortical areas. In the present study, we investigated whether genetic factors associated with this endophenotype could be identified. The study included 420 (age 18–30 years) MA men ( n = 170), and women ( n = 250). DNA was genotyped using an Affymetrix Axiom Exome1A chip. An association analysis revealed that the CCKAR and CCKBR (cholecystokinin A and B receptor) genes each had a nearby variant that showed suggestive significance with the amplitude of the N4S component to pre-pulse stimuli. The neurotransmitter cholecystokinin ( CCK ), along with its receptors, CCKAR and CCKBR , have been previously associated with psychiatric disorders, suggesting that variants near these genes may play a role in the pre-pulse/startle response in this cohort

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease

Functionally Stable and Phylogenetically Diverse Microbial Enrichments from Microbial Fuel Cells during Wastewater Treatment

Microbial fuel cells (MFCs) are devices that exploit microorganisms as biocatalysts to recover energy from organic matter in the form of electricity. One of the goals of MFC research is to develop the technology for cost-effective wastewater treatment. However, before practical MFC applications are implemented it is important to gain fundamental knowledge about long-term system performance, reproducibility, and the formation and maintenance of functionally-stable microbial communities. Here we report findings from a MFC operated for over 300 days using only primary clarifier effluent collected from a municipal wastewater treatment plant as the microbial resource and substrate. The system was operated in a repeat-batch mode, where the reactor solution was replaced once every two weeks with new primary effluent that consisted of different microbial and chemical compositions with every batch exchange. The turbidity of the primary clarifier effluent solution notably decreased, and 97% of biological oxygen demand (BOD) was removed after an 8–13 day residence time for each batch cycle. On average, the limiting current density was 1000 mA/m2, the maximum power density was 13 mW/m2, and coulombic efficiency was 25%. Interestingly, the electrochemical performance and BOD removal rates were very reproducible throughout MFC operation regardless of the sample variability associated with each wastewater exchange. While MFC performance was very reproducible, the phylogenetic analyses of anode-associated electricity-generating biofilms showed that the microbial populations temporally fluctuated and maintained a high biodiversity throughout the year-long experiment. These results suggest that MFC communities are both self-selecting and self-optimizing, thereby able to develop and maintain functional stability regardless of fluctuations in carbon source(s) and regular introduction of microbial competitors. These results contribute significantly toward the practical application of MFC systems for long-term wastewater treatment as well as demonstrating MFC technology as a useful device to enrich for functionally stable microbial populations