Biology subjects

Amir, A.

Publications and source records attributed to Amir, A..

6 recordsLinked to original sources

bioRxiv2018

Length regulation of multiple flagella that self-assemble from a shared pool of components

Control of organelle size is a problem that has intrigued cell biologists for at least a century. The single-celled green algae Chlamydomonas reinhardtii with its two 2agella has proved to be a very useful model organism for studies of size control. Numerous experiments have identi1ed motor-driven transport of tubulin to the growing ends of microtubules at the tip of the 2agella as the key component of the machinery responsible for controlling their length. Here we consider a model of 2agellar length control whose key assumption is that proteins responsible for the intra2agellar transport (IFT) of tubulin are present in limiting amounts. We show that this limiting-pool assumption and simple reasoning based on the law of mass action leads to an inverse relationship between the rate at which a 2agellum grows and its length, which has been observed experimentally, and has been shown theoretically to provide a mechanism for length control. Experiments in which one of the two 2agella are severed have revealed the coupled nature of the growth dynamics of the two 2agella, and we extend our length-control model to two 2agella by considering different mechanisms of their coupling. We describe which coupling mechanisms are capable of reproducing the observed dynamics in severing experiments, and why some that have been proposed previously are not. Within our theoretical framework we conclude that if tubulin and IFT proteins are freely exchanged between 2agella simultaneous length control is not possible if the disassembly rate is constant. However, if disassembly depends on the concentration of IFT proteins at the tip of the 2agellum, simultaneous length control can be achieved. Finally, we make quantitative predictions for experiments that could test this model.

systems biology↗

bioRxiv2018

Bacterial cell lysis: geometry, elasticity, and implications

Membrane lysis, or rupture, is a cell death pathway in bacteria frequently caused by cell wall-targeting antibiotics. Although several studies have clarified biochemical mechanisms of antibiotic action, a physical understanding of the processes leading to lysis remains lacking. Here, we analyze the dynamics of membrane bulging and lysis in Escherichia coli, where, strikingly, the formation of an initial bulge (\"bulging\") after cell wall digestion occurs on a characteristic timescale as fast as 100 ms and the growth of the bulge (\"swelling\") occurs on a slower characteristic timescale of 10-100 s. We show that bulging can be energetically favorable due to the relaxation of the entropic and stretching energies of the inner membrane, cell wall, and outer membrane and that experimentally observed bulge shapes are consistent with model predictions. We then show that swelling can involve both the continued flow of water into the cytoplasm and the enlargement of wall defects, after which cell lysis is consistent with both the inner and outer membranes exceeding characteristic estimates of the yield areal strains of biological membranes. Our results contrast biological membrane physics and the physics of thin shells, reveal principles of how all bacteria likely function in their native states, and may have implications for cellular morphogenesis and antibiotic discovery across different species of bacteria.

biophysics↗

bioRxiv2018

Homeostasis of protein and mRNA concentrations in growing cells

Many experiments show that the numbers of mRNA and protein are proportional to the cell volume in growing cells. However, models of stochastic gene expression often assume constant transcription rate per gene and constant translation rate per mRNA, which are incompatible with these experiments. Here, we construct a minimal gene expression model to fill this gap. Assuming ribosomes and RNA polymerases are limiting in gene expression, we find that (1) because the ribosomes translate all proteins, the concentrations of proteins and mRNAs are regulated in an exponentially growing cell volume; (2) the competition between genes for the RNA polymerases makes the transcription rate independent of the genome number. Furthermore, by extending the model to situations in which DNA (mRNA) can be saturated by RNA polymerases (ribosomes) and becomes limiting, we predict a transition from exponential to linear growth of cell volume as the protein-to-DNA ratio increases.

biophysics↗

bioRxiv2017

MreB Filaments Create Rod Shape By Aligning Along Principal Membrane Curvature.

MreB is essential for rod shape in many bacteria. Membrane-associated MreB filaments move around the rod circumference, helping to insert cell wall in the radial direction to reinforce rod shape. To understand how oriented MreB motion arises, we altered the shape of Bacillus subtilis. MreB motion is isotropic in round cells, and orientation is restored when rod shape is externally imposed. Stationary filaments orient within protoplasts, and purified MreB tubulates liposomes in vitro, orienting within tubes. Together, this demonstrates MreB orients along the greatest principal membrane curvature, a conclusion supported with biophysical modeling. We observed that spherical cells regenerate into rods in a local, self-reinforcing manner: rapidly propagating rods emerge from small bulges, exhibiting oriented MreB motion and increased glycan crosslinking. We propose that the coupling of MreB filament alignment to shape-reinforcing peptidoglycan synthesis creates a locally-acting, self-organizing mechanism allowing the rapid establishment and stable maintenance of emergent rod shape.

microbiology↗

bioRxiv2017

Towards a highly efficient diversity census of the prokaryotic biosphere: a group testing approach

Exploring the microbial biosphere has grown exponentially in recent years, although we are far from understanding its entirety. We present the adiversity censusa problem of exploring all bacterial species in a large cohort of specimens, and detecting a specimen that contains each species. The naive approach to this problem is to sequence each specimen, thus requiring costly sample preparation steps.\n\nWe suggest an orders of magnitude more efficient approach for diversity censusing. Specimens are pooled according to a predefined design and standard 16S rRNA sequencing is performed over each pool. For each bacterial species, from the ultra-rare to the most common, the algorithm detects a single specimen that contains the bacterial species. The approach can be applied to large cohorts of monomicrobial cultures or to complex samples containing a mixture of organisms.\n\nWe model the experimental procedure and show via in silico simulations that the approach enables censusing more than 95% of the species while taking 10 - 70 fold less resources. Simulating experiments using real samples display the utility in censusing large cohorts of samples.\n\nDiversity censusing presents a novel problem in the mathematical field of group testing that may also be applied in other biological problems and in other domains.

microbiology↗

bioRxiv2017

Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling

BackgroundMost of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region and the resolution of profiling is inherently limited. Platforms that offer ultra long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others, all allow different ways to circumvent this problem yet suffer various shortcomings. There is need for a simple and low cost 16S rRNA gene based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.\n\nResultsThis manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about two-fold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200bp (80%) of the 16S rRNA gene we were able to achieve ~100 fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~100 fold increase in resolution, and thus enabling efficient downstream analysis.\n\nConclusionsSMURF enables identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of Formalin-fixed and Paraffin-embedded samples, fossil-derived DNA or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in Multilocus Sequence Typing (MLST).

bioinformatics↗