Biology subjects

Almonacid, D. E.

Publications and source records attributed to Almonacid, D. E..

4 recordsLinked to original sources

bioRxiv2018

IDH1 and IDH2 mutants identified in cancer lose inhibition by isocitrate because of a change in their binding sites

IDH1 and IDH2 are human enzymes that convert isocitrate (ICT) into -ketoglutarate (AKG). However, mutations in positions R132 of IDH1 and R140 and R172 of IDH2 cause these enzymes to convert AKG into 2-hydroxyglutarate (2HG). Concurrently, accumulation of 2HG in the cell is correlated with the development of cancer. This activity change is mainly due to the loss of the competitive inhibition by ICT of these enzymes, but the molecular mechanism behind this loss of inhibition is currently unknown. In this work we characterized the inhibition and loss of inhibition of IDH1 and IDH2 by means of the binding energies derived from molecular docking calculations. We characterized the substrate binding sites and how they differ among the mutant and wild type enzymes using a Jaccard similarity coefficient based on the residues involved in binding the substrates. We found that molecular docking effectively identifies the inhibition by ICT in the wild type and mutant enzymes that do not appear in tumors, and the loss of inhibition in the mutant enzymes that appear in tumors. Additionally, we found that the binding sites of the mutant enzymes are different among themselves. Finally, we found that the regulatory segment of IDH1 plays a prominent role in the change of binding sites between the mutant enzymes and the wild-type enzymes. Our findings show that the loss of inhibition is related to variations in the enzyme binding sites. Additionally, our findings show that a drug capable of targeting all IDH1 and IDH2 mutations in cancer is unlikely to be found due to significant differences among the binding sites of these paralogs. Moreover, the methodology developed here, which combines molecular docking calculations with binding site similarity estimation, can be useful for engineering enzymes, for instance, when aiming to modify the substrate affinity of an enzyme.

bioinformatics↗

bioRxiv2018

Measures of reproducibility in sampling and laboratory processing methods in high-throughput microbiome analysis

Microbial community analysis can be biased by multiple technical factors, such as storage conditions, DNA extraction, or amplification conditions. In a high-throughput laboratory that relies on samples obtained from thousands of different subjects, knowledge of the extent of subject-introduced sampling and storage variation on the outcome of the inferred microbiome, as well as the effect of laboratory-introduced variation caused by reagent batches, equipment, or operator on the consistency of these processes within the laboratory is paramount. Here, we analyzed the effect of sampling from different parts of the same stool specimen or on different consecutive days, as well as short-term storage of samples at different temperatures on microbiome profiles obtained by 16S rRNA gene amplification. Each of these factors had relatively little effect on the microbial composition. In addition, replicate amplification of 44 stool samples showed reproducible results. Finally, 363 independent replicate extractions and amplifications of a single human homogenized stool (HS) specimen showed reproducible results (average Lins correlation = 0.95), with little variation introduced by HS batch, operator, extraction equipment, or DNA sequencer. In all cases, variations between replicates were significantly smaller than those between individual samples; subject identity always was the largest determinant. We propose that homogenized stool specimens could be used as quality control to routinely monitor the laboratory process and to validate new methods.

microbiology↗

bioRxiv2017

A new sequencing-based women’s health assay combining self-sampling, HPV detection and genotyping, STI detection, and vaginal microbiome analysis

The composition of the vaginal microbiome, including both the presence of pathogens involved in sexually transmitted infections (STI) as well as commensal microbiota, has been shown to have important associations for a womans reproductive and general health. Currently, healthcare providers cannot offer comprehensive vaginal microbiome screening, but are limited to the detection of individual pathogens, such as high-risk human papillomavirus (hrHPV), the predominant cause of cervical cancer. There is no single test on the market that combines HPV, STI, and microbiome screening. Here, we describe a novel inclusive womens health assay that combines self-sampling with sequencing-based HPV detection and genotyping, vaginal microbiome analysis, and STI-associated pathogen detection. The assay includes genotyping and detection of 14 hrHPV types, 5 low-risk HPV types (lrHPV), as well as the relative abundance of 32 bacterial taxa of clinical importance, including Lactobacillus, Sneathia, Gardnerella, and 4 pathogens involved in STI, with high sensitivity, specificity, and reproducibility. For each of these taxa, healthy ranges were determined in a group of 50 self-reported healthy women. The hrHPV portion of the test was evaluated against the Digene High-Risk HPV HC2 DNA test with vaginal samples obtained from 185 women. Results were concordant for 181/185 of the samples (overall agreement of 97.83%, Cohens kappa = 0.93), with sensitivity and specificity values of 94.74% and 98.64%, respectively. Two discrepancies were caused by the Digene assays known cross-reactivity with low-risk HPV types, while two additional samples were found to contain hrHPV not detected by Digene. This novel assay could be used to complement conventional cervical cancer screening, because its self-sampling format can expand access among women who would otherwise not participate, and because of its additional information about the composition of the vaginal microbiome and the presence of pathogens.

microbiology↗

bioRxiv2016

16S rRNA Gene Sequencing as a Clinical Diagnostic Aid for Gastrointestinal-related Conditions

Accurate detection of the microorganisms underlying gut dysbiosis in the patient is critical to initiate the appropriate treatment. However, most clinical microbiology techniques used to detect gut bacteria were developed over a century ago and rely on culture-based approaches that are often laborious, unreliable, and subjective. Further, culturing does not scale well for multiple targets and detects only a minority of the microorganisms in the human gastrointestinal tract. Here we present a clinical test for gut microorganisms based on targeted sequencing of the prokaryotic 16S rRNA gene. We tested 46 clinical prokaryotic targets in the human gut, 28 of which can be identified by a bioinformatics pipeline that includes sequence analysis and taxonomic annotation. Using microbiome samples from a cohort of 897 healthy individuals, we established a reference range defining clinically relevant relative levels for each of the 28 targets. Our assay accurately quantified all 28 targets and correctly reflected 38/38 verification samples of real and synthetic stool material containing known pathogens. Thus, we have established a new test to interrogate microbiome composition and diversity, which will improve patient diagnosis, treatment and monitoring. More broadly, our test will facilitate epidemiological studies of the microbiome as it relates to overall human health and disease.

microbiology↗