Mershin, A. | Search bioRxiv

bioRxiv2020

Diffusion Fails to Make a Stink

In this work we consider the question of whether a simple diffusive model can explain the scent tracking behaviors found in nature. For such tracking to occur, both the concentration of a scent and its gradient must be above some threshold. Applying these conditions to the solutions of various diffusion equations, we find that a purely diffusive model cannot simultaneously satisfy the tracking conditions when parameters are in the experimentally observed range. This demonstrates the necessity of modelling odor dispersal with full fluid dynamics, where non-linear phenomena such as turbulence play a critical role.

biophysics↗

bioRxiv2020

Genetic requirements for cell division in a genomically minimal cell

Genomically minimal cells, such as JCVI-syn3.0, offer a platform to clarify genes underlying core physiological processes. While this minimal cell includes genes essential for population growth, the physiology of its single cells remained uncharacterized. To investigate striking morphological variation in JCVI-syn3.0 cells, we present an approach to characterize cell propagation and determine genes affecting cell morphology. Microfluidic chemostats allowed observation of intrinsic cell dynamics resulting in irregular morphologies. The addition of 19 genes not retained in JCVI-syn3.0 generated JCVI-syn3A, which presents significantly less morphological variation than JCVI-syn3.0. We further identified seven of these 19 genes, including two known cell division genes ftsZ and sepF and five genes of unknown function, required together to restore cell morphology and division similar to JCVI-syn1.0. This surprising result emphasizes the polygenic nature of cell morphology, as well as the importance of a Z-ring and membrane properties in the physiology of genomically minimal cells.

synthetic biology↗

bioRxiv2020

Feasibility of Integrating Canine Olfaction with Chemical and Microbial Profiling of Urine to Detect Lethal Prostate Cancer

Prostate cancer is the second leading cause of cancer death in men in the developed world. A more sensitive and specific detection strategy for lethal prostate cancer beyond serum prostate specific antigen (PSA) population screening is urgently needed. Diagnosis by canine olfaction, using dogs trained to detect cancer by smell, has been shown to be both specific and sensitive. While dogs themselves are impractical as scalable diagnostic sensors, machine olfaction for cancer detection is testable. However, studies bridging the divide between clinical diagnostic techniques, artificial intelligence, and molecular analysis remains difficult due to the significant divide between these disciplines. We tested the clinical feasibility of a cross-disciplinary, integrative approach to early prostate cancer biosensing in urine using trained canine olfaction, volatile organic compound (VOC) analysis by gas chromatography-mass spectroscopy (GC-MS) artificial neural network (ANN)-assisted examination, and microbial profiling in a double-blinded pilot study. Two dogs were trained to detect Gleason 9 prostate cancer in urine collected from biopsy-confirmed patients. Biopsy-negative controls were used to assess canine specificity as prostate cancer biodetectors. Urine samples were simultaneously analyzed for their VOC content in headspace via GC-MS and urinary microbiota content via 16S rDNA Illumina sequencing. In addition, the dogs diagnoses were used to train an ANN to detect significant peaks in the GC-MS data. The canine olfaction system was 71% sensitive and between 70-76% specific at detecting Gleason 9 prostate cancer. We have also confirmed VOC differences by GC-MS and microbiota differences by 16S rDNA sequencing between cancer positive and biopsy-negative controls. Furthermore, the trained ANN identified regions of interest in the GC-MS data, informed by the canine diagnoses. Methodology and feasibility are established to inform larger-scale studies using canine olfaction, urinary VOCs, and urinary microbiota profiling to develop machine olfaction diagnostic tools. Scalable multi-disciplinary tools may then be compared to PSA screening for earlier, non-invasive, more specific and sensitive detection of clinically aggressive prostate cancers in urine samples.

cancer biology↗