Biology subjects

Kogan, D.

Publications and source records attributed to Kogan, D..

3 recordsLinked to original sources

bioRxiv2024

A hollow fiber membrane-based liver organoid-on-a-chip model for examining drug metabolism and transport

Liver-on-a-chip models predictive for both metabolism as well as canalicular and blood transport of drug candidates in humans are lacking. Here, we established an advanced, bioengineered and animal component-free hepatocyte-like millifluidic system based on 3D hollow fiber membranes (HFMs), recombinant human laminin 332 coating and adult human stem cell-derived organoids. Organoid fragments formed polarized and tight monolayers on HFMs with improved hepatocyte-like maturation, as compared to standard 3D organoid cultures in Matrigel from matched donors. Gene expression profiling and immunofluorescence revealed that hepatocyte-like monolayers expressed a broad panel of phase I (e.g., CYP3A4, CYP2D6) and II (UGTs, SULTs) drug-metabolizing enzymes and drug transporters (e.g., OATP1B3, MDR1 and MRP3). Moreover, statically cultured monolayers displayed phase I and II metabolism of a cocktail of six relevant compounds, including midazolam and 7-hydroxycoumarin. We also demonstrated the disposition of midazolam in the basal/blood-like circulation and apical/canalicular-like compartment of the millifluidic chip. Finally, we connected the system to the other two PK/ADME-most relevant organ systems, i.e. small intestine- and kidney proximal tubule-like to study the bioavailability of midazolam and coumarin, and excretion of metformin. In conclusion, we generated a proof-of-concept liver organoid-on-a-chip model for examining metabolism and transport of drugs, which can be further developed to predict PK/ADME profiles in humans.

pharmacology and toxicology↗

bioRxiv2023

Correlation of coalescence times in a diploid Wright-Fisher model with recombination and selfing

The correlation among the gene genealogies at different loci is crucial in biology, yet challenging to understand because such correlation depends on many factors including genetic linkage, recombination, natural selection and population structure. Based on a diploid Wright-Fisher model with a single mating type and partial selfing for a constant large population with size N, we quantify the combined effect of genetic drift and two competing factors, recombination and selfing, on the correlation of coalescence times at two linked loci for samples of size two. Recombination decouples the genealogies at different loci and decreases the correlation while selfing increases the correlation. We obtain explicit asymptotic formulas for the correlation for four scaling scenarios that depend on whether the selfing probability and the recombination probability are of order O(1/N) or O(1) as N tends to infinity. Our analytical results confirm that the asymptotic lower bound in [King, Wakeley, Carmi (TPB 2018)] is sharp when the loci are unlinked and when there is no selfing, and provide a number of new formulas for other scaling scenarios that have not been considered before. We present asymptotic results for the variance of Tajimas estimator of the population mutation rate for infinitely many loci as N tends to infinity. When the selfing probability is of order O(1) and is equal to a positive constant s for all N and if the samples at both loci are in the same individual, then the variance of the Tajimas estimator tends to s/2 (hence remains positive) even when the recombination rate, the number of loci and the population size all tend to infinity.

evolutionary biology↗

bioRxiv2017

TraPS-VarI: a python module for the identification of STAT3 modulating germline receptor variants

MotivationHuman individuals differ because of variations in the DNA sequences of all the 46 chromosomes. Information on genetic variations altering the membrane-proximal binding sites for signal transducer of transcription 3 (STAT3) is valuable for understanding the genetic basis of cancer prognosis and disease progression (Ulaganathan et al, 2015). In this regard, non-synonymous coding region mutations resulting in the alteration of protein sequence in the juxtamembrane region of the type I membrane proteins are biologically and clinically relevant. The knowledge of such rare cell line- and individual-specific germline receptor variants is crucial for the investigation of cell-line specific biological mechanisms and genotype-centric therapeutic approaches.\n\nResultsHere we present TraPS-VarI (Transmembrane Protein Sequence Variant Identifier), a python module to rapidly identify human germline receptor variants modulating STAT3 binding sites by using the genetic variation datasets in the variant call format 4.0. For the found protein variants the module also checks for the availability of associated therapeutic agents and ongoing clinical trial studies.\n\nAvailabilityThe Source code and binaries are freely available for download at https://gitlab.com/VJ-Ulaganathan/TraPS-VarI and the documentation can be found at http://traps-vari.readthedocs.io/.\n\nContactulaganat@biochem.mpg.de & ulaganat@icloud.com\n\nSupplementary informationSupplementary data enclosed with the manuscript file.

bioinformatics↗