Biology subjects

Amir, A.

Publications and source records attributed to Amir, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Computational insights on the destabilizing mutations in the binding site of 3CL-protease SARS-CoV-2 Omicron (VOC)

The COVID-19 (Corona Virus Disease 19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is causing enormous difficulties in the worlds economies and there is uncertainty as to whether the current prophylactic measures will offer adequate protection globally after the appearance of virus variants that like that indicated as Omicron emerged in the presence of global vaccine-based immunization. While several studies are available describing the main differences in the spike protein of Omicron compared to the other variants previously emerged, there was no structural insights into the 3CL-protease (3CLpro) associated to the new variant. Herein, we performed a computational study based on genomic data and amino acid sequences available in the most updated COVID-19-related databases that allowed us to build up in silico the 3D structure of Omicron 3CLpro. Moreover, by molecular dynamics simulation we demonstrated that currently available drugs acting as inhibitors of the SARS-CoV-2 main protease could be less effective in the case of Omicron variant due to the different chemical interactions in the binding site occurred after the recent amino acid mutations. Ultimately, our study highlights the need of exploiting in silico and in vitro methods to discover novel 3CLpro inhibitors starting from the computationally based structure we presented herein, and more in general to direct the major efforts to targeting the most conserved 3CLpro regions that appeared unchanged in the context of the Omicron variant.

bioinformatics↗

bioRxiv2023

Mycobacterium tuberculosis grows linearly at the single-cell level with larger variability than model organisms

The ability of bacterial pathogens to regulate growth is crucial to control homeostasis, virulence, and drug response. Yet, we do not understand the growth and cell cycle behaviors of Mycobacterium tuberculosis (Mtb), a slow-growing pathogen, at the single-cell level. Here, we use time-lapse imaging and mathematical modeling to characterize these fundamental properties of Mtb. Whereas most organisms grow exponentially at the single-cell level, we find that Mtb exhibits a unique linear growth mode. Mtb growth characteristics are highly variable from cell-to-cell, notably in their growth speeds, cell cycle timing, and cell sizes. Together, our study demonstrates that growth behavior of Mtb diverges from what we have learned from model bacteria. Instead, Mtb generates a heterogeneous population while growing slowly and linearly. Our study provides a new level of detail into how Mtb grows and creates heterogeneity, and motivates more studies of growth behaviors in bacterial pathogens.

microbiology↗

bioRxiv2023

Physiological stress drives the emergence of a Salmonella subpopulation through ribosomal RNA regulation

Bacteria undergo cycles of growth and starvation, to which they must adapt swiftly. One important strategy for adjusting growth rates relies on ribosomal levels. While high ribosomal levels are required for fast growth, their dynamics during starvation remain unclear. Here, we analyzed ribosomal RNA (rRNA) content of individual Salmonella cells using Fluorescence In-Situ Hybridization (rRNA-FISH). During the transition from exponential to stationary phase we measured a dramatic decrease in rRNA numbers only in a subpopulation, resulting in a bimodal distribution of cells with high and low rRNA content. We showed that the two subpopulations are phenotypically distinct when subjected to nutritional upshifts. Using a transposon screen coupled with rRNA-FISH, we identified two mutants acting on rRNA transcription shutdown and degradation, that abolished the formation of the subpopulation with low rRNA content. Our work suggests that Salmonella employs a bet-hedging strategy in regulating ribosomal levels that may be beneficial for survival.

microbiology↗

bioRxiv2023

In-silico Analysis of SARS-Cov2 Spike Proteins of Different Field Variants

BackgroundCoronaviruses belong to the group of RNA family of viruses which trigger diseases in birds, humans, and mammals, which can cause respiratory tract infections. The COVID-19 pandemic has badly affected every part of the world, and the situation in the world is getting worse with the emergence of novel variants. Our study aims to explore the genome of SARS-,CoV2 followed by in silico analysis of its proteins. MethodsDifferent nucleotide and protein variants of SARS-Cov2 were retrieved from NCBI. Contigs & consensus sequences were developed to identify variations in these variants by using SnapGene. Data of variants that significantly differ from each other was run through Predict Protein software to understand changes produced in protein structure The SOPMA web server was used to predict the secondary structure of proteins. Tertiary structure details of selected proteins were analyzed using the online web server SWISS-MODEL. FindingsSequencing results shows numerous single nucleotide polymorphisms in surface glycoprotein, nucleocapsid, ORF1a, and ORF1ab polyprotein. While envelope, membrane, ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes have no or few SNPs. Contigs were mto identifyn of variations in Alpha & Delta Variant of SARs-CoV-2 with reference strain (Wuhan). The secondary structures of SARs-CoV-2 proteins were predicted by using sopma software & were further compared with reference strain of SARS-CoV-2 (Wuhan) proteins. The tertiary structure details of only spike proteins were analyzed through the SWISS-MODEL and Ramachandran plot. By Swiss-model, a comparison of the tertiary structure model of SARS-COV-2 spike protein of Alpha & Delta Variant was made with reference strain (Wuhan). Alpha & Delta Variant of SARs-CoV-2 isolates submitted in GISAID from Pakistan with changes in structural and nonstructural proteins were compared with reference strain & 3D structure mapping of spike glycoprotein and mutations in amino acid were seen. ConclusionThe surprising increased rate of SARS-CoV-2 transmission has forced numerous countries to impose a total lockdown due to an unusual occurrence. In this research, we employed in silico computational tools to analyze SARS-CoV-2 genomes worldwide to detect vital variations in structural proteins and dynamic changes in all SARS-CoV-2 proteins, mainly spike proteins, produced due to many mutations. Our analysis revealed substantial differences in functional, immunological, physicochemical, & structural variations in SARS-CoV-2 isolates. However real impact of these SNPs can only be determined further by experiments. Our results can aid in vivo and in vitro experiments in the future.

bioinformatics↗

bioRxiv2023

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the populations mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn.

evolutionary biology↗

bioRxiv2022

Pangenome analysis of SARS-CoV2 strains to Identify Potential vaccine targets by Reverse Vaccinology

BackgroundCoronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) leads to respiratory failure and obstructive alveolar damage, which may be fatal in immunocompromised individuals. COVID-19 pandemic has severe global implications badly, and the situation in the world is depreciating with the emergence of novel variants. The aim of our study is to explore the genome of SARS-CoV2 followed by in silico reverse vaccinology analysis. This will help to identify the most putative vaccine candidate against the virus in a robust manner and enables cost-effective development of vaccines compared with traditional strategies. MethodsThe genomic sequencing data is retrieved from NCBI (Reference Sequence Number NC_045512.2). The sequences are explored through comparative genomics approaches by GENOMICS to find out the core genome. The comprehensive set of proteins obtained was employed in computational vaccinology approaches for the prediction of the best possible B and T cell epitopes through ABCpred and IEDB Analysis Resource, respectively. The multi-epitopes were further tested against human toll-like receptor and cloned in E. coli plasmid vector. FindingsThe designed Multiepitope Subunit Vaccine was non-allergenic, antigenic (0.6543), & non-toxic, with significant connections with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 84.38%. It has 276 amino acids, consisting of an adjuvant with the aid of EAAAK linker, AAY linkers used to join the 4 CTL epitopes, GPGPG linkers used to join the 3 HTL epitopes and KK linkers used to join the 7 B-cell epitopes. MESV docking with human pathogenic toll-like receptors-3 (TLR3) exhibited a stable & high binding affinity. An in-silico codon optimization approach was used in the codon system of E. coli (strain K12) to obtain the GC-Content of Escherichia coli (strain K12): 50.7340272413779 and CAI-Value of the improved sequence: 0.9542834278823386. The multi-epitope vaccines optimized gene sequence was cloned in-silico in E. coli plasmid vector pET-30a (+), BamHI and HindIII restriction sites were added to the N and C-terminals of the sequence, respectively. ConclusionThere is a pressing need to combat COVID-19 and we need quick and reliable approaches against Covid-19. By using In-silico approaches, we acquire an effective vaccine that could trigger adequate immune responses at the cellular and humoral level. The suggested sequences can be further validated through in vivo and in vitro experimentation. Statement of SignificanceCurrent developments in the immunological bioinformatics areas has resulted in different servers and tools that are cost and time efficient for the traditional vaccine development. Though for designing a multiple epitope vaccine the antigenic epitopes prediction of a relevant protein by immunoinformatic methods are very helpful.

bioinformatics↗

bioRxiv2022

Evolutionary dynamics in non-Markovian models of microbial populations

In the past decade, great strides have been made to quantify the dynamics of single-cell growth and division in microbes. In order to make sense of the evolutionary history of these organisms, we must understand how features of single-cell growth and division influence evolutionary dynamics. This requires us to connect processes on the single-cell scale to population dynamics. Here, we consider a model of microbial growth in finite populations which explicitly incorporates the single-cell dynamics. We study the behavior of a mutant population in such a model and ask: can the evolutionary dynamics be coarse-grained so that the forces of natural selection and genetic drift can be expressed in terms of the long-term fitness? We show that it is in fact not possible, as there is no way to define a single fitness parameter (or reproductive rate) that defines the fate of an organism even in a constant environment. This is due to fluctuations in the population averaged division rate. As a result, various details of the single-cell dynamics affect the fate of a new mutant independently from how they affect the long-term growth rate of the mutant population. In particular, we show that in the case of neutral mutations, variability in generation times increases the rate of genetic drift, and in the case of beneficial mutations, variability decreases its fixation probability. Furthermore, we explain the source of the persistent division rate fluctuations and provide analytic solutions for the fixation probability as a multi-species generalization of the Euler-Lotka equation.

evolutionary biology↗

bioRxiv2022

Single-cell microfluidic analysis unravels individual cellular fates during Double-Strand Break Repair

Trinucleotide repeat expansions are responsible for two dozen human disorders. Contracting expanded repeats by Double-Strand Break Repair (DSBR) might be a therapeutic approach. Given the complexity of manipulating human cells, recent assays were made to quantify DSBR efficacy in yeast, using a fluorescent reporter. In this study DSBR is characterized with an interdisciplinary approach, linking large population dynamics and individual cells. Time-resolved molecular measurements of changes in the population are first confronted to a coupled differential equation model to obtain repair processes rates. Comparisons with measurements in microfluidic devices, where the progeny of 80-150 individual cells are followed, show good agreement between individual trajectories and mathematical and molecular results. Further analysis of individual progenies shows the heterogeneity of individual cell contributions to global repair efficacy. Three different categories of repair are identified: high-efficacy error-free, low-efficacy error-free and low-efficacy error-prone. These categories depend on the type of endonuclease used and on the target sequence.

bioengineering↗

bioRxiv2022

Achieving pan-microbiome biological insights via the dbBact knowledge base

16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying the microbial world. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when analyzing their findings and interpret them in a broader context. To bridge this gap, we introduce dbBact, an open wiki-like bacterial knowledge base. dbBact combines information from hundreds of studies across diverse habitats, creating a collaborative central repository where 16S rRNA amplicon sequence variants (ASVs) are manually extracted from each study and assigned multiple ontology-based terms. Using the >900 studies of dbBact, covering more than 1,400,000 associations between 345,000 ASVs and 6,500 ontology terms, we show how the dbBact statistical and programmatic pipeline can augment standard microbiome analysis. We use multiple examples to demonstrate how dbBact leads to formulating novel hypotheses regarding inter-host similarities, intra-host sources of bacteria, and commonalities across different diseases, and helps detect environmental sources and identify contaminants.

microbiology↗

bioRxiv2022

INFLUENCE OF RAT CENTRAL THALAMIC NEURONS ON FORAGING BEHAVIOR IN A HAZARDOUS ENVIRONMENT

The basolateral amygdala (BL) is a major regulator of foraging behavior. Following BL inactivation, rats become indifferent to predators. However, at odds with the view that the amygdala detects threats and generate defensive behaviors, most BL neurons have reduced firing rates during foraging and at proximity of the predator. In search of the signals determining this unexpected activity pattern, this study considered the contribution of the central medial thalamic nucleus (CMT), which sends a strong projection to BL, mostly targeting its principal neurons. Inactivation of CMT or BL with muscimol abolished the rats normally cautious behavior in the foraging task. Moreover, unit recordings revealed that CMT neurons showed large but heterogeneous activity changes during the foraging task, with many neurons decreasing or increasing their discharge rates, with a modest bias for the latter. A generalized linear model revealed that CMT neurons encode many of the same task variables as principal BL cells. However, the nature (inhibitory vs. excitatory) and relative magnitude of the activity modulations seen in CMT neurons differed markedly from those of principal BL cells but were very similar to those of fast-spiking BL interneurons. Together, these findings suggest that, during the foraging task, CMT inputs fire some principal BL neurons, recruiting feedback interneurons in BL, resulting in the widespread inhibition of principal BL cells.

neuroscience↗

bioRxiv2021

The effect of weak clonal interference on average fitness trajectories in the presence of macroscopic epistasis

Adaptation dynamics on fitness landscapes is often studied theoretically in the strong-selection, weak-mutation (SSWM) regime. However, in a large population, multiple beneficial mutants can emerge before any of them fixes in the population. Competition between mutants is known as clonal interference, and how it affects the form of long-term fitness trajectories in the presence of epistasis is an open question. Here, by considering how changes in fixation probabilities arising from weak clonal interference affect the dynamics of adaptation on fitness-parameterized landscapes, we find that the change in the form of fitness trajectory arises only through changes in the supply of beneficial mutations (or equivalently, the beneficial mutation rate). Furthermore, a depletion of beneficial mutations as a population climbs up the fitness landscape can speed up the functional form of the fitness trajectory, while an enhancement of the beneficial mutation rate does the opposite of slowing down the form of the dynamics. Our findings suggest that by carrying out evolution experiments in both regimes (with and without clonal interference), one could potentially distinguish the different sources of macroscopic epistasis (fitness effect of mutations vs. change in fraction of beneficial mutations).

genetics↗

bioRxiv2021

To bin or not to bin: analyzing single-cell growth data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

microbiology↗

bioRxiv2021

Replication-related control over cell division in Escherichia coli is growth-rate dependent

How replication and division processes are coordinated in the cell cycle is a fundamental yet poorly understood question in cell biology. In Escherichia coli different data sets and models have supported a range of conclusions from one extreme where these two processes are tightly linked to another extreme where these processes are completely independent of each other. Using high throughput optical microscopy and cell cycle modeling, we show that in slow growth conditions replication and division processes are strongly correlated, indicating a significant coupling between replication and division. This coupling weakens as the growth rate of cells increases. Our data suggest that the underlying control mechanism in slow growth conditions is related to unreplicated chromosome blocking the onset of constriction at the midcell. We show that the nucleoid occlusion protein SlmA does not play a role in this process and neither do other known factors involved in positioning bacterial Z-ring relative to the chromosome. Altogether this work reconciles different ideas from the past and brings out a more nuanced role of replication in controlling the division process in a growth-rate dependent manner.

cell biology↗

bioRxiv2020

Modeling the impact of single-cell stochasticity and size control on the population growth rate in asymmetrically dividing cells

Microbial populations show striking diversity in cell growth morphology and lifecycle [1]; however, our understanding of how these factors influence the growth rate of cell populations remains limited. We use theory and simulations to predict the impact of asymmetric cell division, cell size regulation and single-cell stochasticity on the population growth rate. Our model predicts that coarse-grained noise in the single-cell growth rate {lambda} decreases the population growth rate, as previously seen for symmetrically dividing cells [2]. However, for a given noise in {lambda} we find that dividing asymmetrically can enhance the population growth rate for cells with strong size control (between a "sizer" and an "adder"). To reconcile this finding with the abundance of symmetrically dividing organisms in nature, we propose that additional constraints on cell growth and division must be present which are not included in our model, and we explore the effects of selected extensions thereof. Further, we find that within our model, epigenetically inherited generation times may arise due to size control in asymmetrically dividing cells, providing a possible explanation for recent experimental observations in budding yeast [3]. Taken together, our findings provide insight into the complex effects generated by non-canonical growth morphologies.

microbiology↗

bioRxiv2020

Disentangling intrinsic and extrinsic gene expression noise in growing cells

Gene expression is a stochastic process. Despite the increase of protein numbers in growing cells, the protein concentrations are often found to be confined within small ranges throughout the cell cycle. Considering the time trajectory of protein concentration as a random walker in the concentration space, an effective restoring force (with a corresponding "spring constant") must exist to prevent the divergence of concentration due to random uctuations. In this work, we prove that the magnitude of the effective spring constant is directly related to the fraction of intrinsic noise in the total protein concentration noise. We show that one can infer the magnitude of intrinsic, extrinsic, and measurement noises of gene expression solely based on time-resolved data of protein concentration, without any a priori knowledge of the underlying gene expression dynamics. We apply this method to experimental data of single-cell bacterial gene expression. The results allow us to estimate the average protein number and the translation burst parameter.

genetics↗

bioRxiv2020

Cell size regulation in budding yeast does not depend on linear accumulation of Whi5

Cells must couple cell cycle progress to their growth rate to restrict the spread of cell sizes present throughout a population. Linear, rather than exponential, accumulation of Whi5, was proposed to provide this coordination by causing a higher Whi5 concentration in cells born at smaller size. We tested this model using the inducible GAL1 promoter to make the Whi5 concentration independent of cell size. At an expression level that equalizes the mean cell size with that of wild-type cells, the size distributions of cells with galactose-induced Whi5 expression and wild-type cells are indistinguishable. Fluorescence microscopy confirms that the endogenous and GAL1 promoters produce different relationships between Whi5 concentration and cell volume without diminishing size control in the G1 phase. We also expressed Cln3 from the GAL1 promoter, finding that the spread in cell sizes for an asynchronous population is unaffected by this perturbation. Our findings contradict the previously proposed model for cell size control in budding yeast and demonstrate the need for a molecular mechanism that explains how cell size controls passage through Start. Author ContributionsFB performed the experiments, data analysis and simulations. All authors designed the experiments and wrote the manuscript. Significance StatementDespite decades of research, the question of how single cells regulate their size remains unclear. Here we demonstrate that a widely supported molecular model for the fundamental origin of size control in budding yeast is inconsistent with a set of experiments testing the models key prediction. We therefore conclude that the problem of cell size control in budding yeast remains unsolved. This work highlights the need for rigorous testing of future models of size control in order to make progress on this fundamental question.

cell biology↗

bioRxiv2019

Dynamics of diffusive cell signaling relays

Cells can communicate with each other by emitting diffusible signaling molecules into the surrounding environment. However, simple diffusion is slow. Even small molecules take hours to diffuse millimeters away from their source, making it difficult for thousands of cells to coordinate their activity over millimeters, as happens routinely during development and immune response. Moreover, simple diffusion creates shallow, Gaussian-tailed concentration profiles. Attempting to move up or down such shallow gradients - to chemotax - is a difficult task for cells, as they see only small spatial and temporal concentration changes. Here, we demonstrate that cells utilizing diffusive relays, in which the presence of one type of diffusible signaling molecule triggers participation in the emission of the same type of molecule, can propagate fast-traveling diffusive waves that give rise to steep chemical gradients. Our methods are general and capture the effects of dimensionality, cell density, signaling molecule degradation, pulsed emission, and cellular chemotaxis on the diffusive wave dynamics. We show that system dimensionality - the size and shape of the extracellular medium and the distribution of the cells within it - can have a particularly dramatic effect on wave initiation and asymptotic propagation, and that these dynamics are insensitive to the details of cellular activation. As an example, we show that neutrophil swarming experiments exhibit dynamical signatures consistent with the proposed signaling motif. Interpreted in the context of these experiments, our results provide insight into the utility of signaling relays in immune response.

biophysics↗

bioRxiv2019

Evolution of microbial growth traits under serial dilution

Selection of mutants in a microbial population depends on multiple cellular traits. In serial-dilution evolution experiments, three key traits are the lag time when transitioning from starvation to growth, the exponential growth rate, and the yield (number of cells per unit resource). Here we investigate how these traits evolve in laboratory evolution experiments using a minimal model of population dynamics, where the only interaction between cells is competition for a single limiting resource. We find that the fixation probability of a beneficial mutation depends on a linear combination of its growth rate and lag time relative to its immediate ancestor, even under clonal interference. The relative selective pressure on growth rate and lag time is set by the dilution factor; a larger dilution factor favors the adaptation of growth rate over the adaptation of lag time. The model shows that yield, however, is under no direct selection. We also show how the adaptation speeds of growth and lag depend on experimental parameters and the underlying supply of mutations. Finally, we investigate the evolution of covariation between these traits across populations, which reveals that the population growth rate and lag time can evolve a nonzero correlation even if mutations have uncorrelated effects on the two traits. Altogether these results provide useful guidance to future experiments on microbial evolution.

evolutionary biology↗