Biology subjects

Sindi, S.

Publications and source records attributed to Sindi, S..

6 recordsLinked to original sources

bioRxiv2023

Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro

Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides. Unfortunately, patients are often misdiagnosed with bacterial pneumonia leading to inappropriate antibiotic treatment. Soil bacteria B. subtilis-like species exhibits antagonistic properties against Coccidioides in vitro; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro. We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2xGYE (GYE) and CNA w/ 5% sheeps blood agar (5%SB-CNA) inhibited the growth of Coccidioides, but that grown on chocolate agar does not. Partial depletion of the microbiota through antibiotic disk diffusion revealed that microbiota depletion leads to diminished inhibition and comparable growth of Coccidioides growth to controls. To characterize the bacteria grown and narrow down potential candidates contributing to the inhibition of Coccidioides, 16s rRNA sequencing of tracheal and intestinal agar cultures and murine lung extracts was performed. The identity of host bacteria that may be responsible for this inhibition was revealed. The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo. ImportanceCoccidioidomycosis is caused by a fungal pathogen that invades host lungs, causing respiratory distress. In 2019, 20,003 cases of Valley fever were reported to the CDC. However, this number likely vastly underrepresents the true number of Valley fever cases as many go undetected due to poor testing strategies and lack of diagnostic models. Valley fever is also often misdiagnosed as bacterial pneumonia, resulting in 60-80% of patients being treated with antibiotics prior to accurate diagnosis. Misdiagnosis contributes to a growing problem of antibiotic resistance and antibiotic induced microbiome dysbiosis, and the implications on disease outcome are currently unknown. 5%-10% of symptomatic Valley fever patients develop disseminated and/or chronic disease. Valley fever causes a significant financial burden and reduced quality of life. Little is known regarding what factors contribute to the development of chronic infection and treatments for disease are limited.

microbiology↗

bioRxiv2023

Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention

A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scR-NAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNAs performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.

bioinformatics↗

bioRxiv2022

Low normal factor V enhances thrombin generation in hemophilia A through a substrate competition mechanism with factor Xa

Bleeding patterns in people with hemophilia A cannot be predicted solely by factor VIII (FVIII) levels. Some of the variance in bleeding may be attributed to differences in plasma protein composition, and specifically other coagulation factors where the normal ranges span 50-150% of the population mean. We recently used a mathematical model of thrombus formation that identified factor V (FV) levels as a strong modifier of thrombin generation in FVIII deficiencies. Counterintuitively, the model predicted low normal FV levels enhanced thrombin generation. Here, we tested this prediction and investigated its mechanism. Thrombin generation in plasma from people with FVIII deficiencies (<5%) were negatively correlated with FV levels. A substrate competition mechanism wherein FV and FVIII compete for activation by FXa during the initiation of coagulation was tested in three models: In a purified system containing only FV, FVIII, and FXa, reducing FV enhanced FVIII activation. In synthetic plasma containing the essential proteins of the extrinsic coagulation pathway, low normal FV levels resulted in enhanced thrombin generation both in the presence or absence of TFPI. In mixture studies using FVIII-deficient human plasma immunodepleted of FV, thrombin generation was enhanced at lower levels of FV. In all models the trend was nonlinear as the effect size was significant at low, but not high, FV levels. Our data show that low normal plasma levels of FV enhance thrombin generation in hemophilia A by reducing FXa substrate competition for FVIII activation and implicate FV levels as a strong modifier of bleeding in hemophilia A. Key PointsO_LILow normal levels of FV enhance thrombin generation in hemophilia A by reducing substate competition for FVIII activation. C_LIO_LIPlasma FV levels are a strong modifier of bleeding in hemophilia A. C_LI

biochemistry↗

bioRxiv2021

Going through phages: A Computational approach to Revealing the role of prophage in Staphylococcus aureus

Prophages have important roles in virulence, antibiotic resistance and genome evolution in Staphylococcus aureus. Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences in S. aureus at an unprecedented scale. We developed a computational pipeline to detect and analyze prophage sequences in nearly 10,011 S. aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance. ImportanceBacteriophages (phages) play key roles in bacterial evolution, governing abundance, adaptation and diversity of bacterial communities. Temperate phage can facilitate bacterial adaptation via transduction of novel genes. This study takes advantage of the unprecedented quantity of genomic sequencing in public repositories to analyze viral genes in 10,000 Staphylococcus aureus genomes. We found 196,727 prophage sequences, with an estimated total of 129,935 genes. We determined the function of these genes, identifying a large quantity of novel genes that benefit the host such as beta-lactamase, enterotoxins and cytotoxins. These results will inform studies of bacterial evolution and adaptation, by identifying the mechanism of horizontal transfer of genes that confer adaptive traits to bacteria, especially in the context of antibiotic resistance.

microbiology↗

bioRxiv2020

Evidence for prescribed NK cell Ly49 developmental pathways in mice

Previous studies of NK cell inhibitory Ly49 receptors suggested their expression is stochastic. However, relatively few studies have examined this stochasticity in conjunction with activating Ly49 receptors. We hypothesized that the expression of activating Ly49 receptors is not stochastic and is influenced by inhibitory Ly49 receptors. We analyzed NK cell "clusters" defined by combinatorial expression of activating (Ly49H, Ly49D) and inhibitory (Ly49I, Ly49G2) receptors in C57BL/6 mice. Using the product rule to evaluate the interdependencies of the Ly49 receptors, we found evidence for a tightly regulated expression at the immature NK cell stage, with the highest interdependencies between clusters that express at least one activating receptor. Further analysis demonstrated that certain NK clusters predominated at the immature (CD27+CD11b-), transitional (CD27+CD11b+) and mature (CD27-CD11b-) NK cell stages. Using parallel in vitro culture and in vivo transplantation of sorted NK clusters, we discovered non-random upregulation of Ly49 receptors, suggesting that prescribed pathways of NK cluster differentiation exist. Our data infer that upregulation of Ly49I is an important step in NK cell maturation. Ki-67 expression and cell counts confirmed that immature NK cells proliferate more than mature NK cells. We found that MHC-I is particularly important for regulation of Ly49D and Ly49G2, even though no known MHC-I ligand for these receptors is present in B6 mice. Our data indicate that the regulatory systems controlling the expression of both activating and inhibitory Ly49 receptors are non-stochastic and support the idea that NK cell clusters develop in a non-random process correlated to their maturation stage.

immunology↗

bioRxiv2020

A unifying model for the propagation of prion proteins in yeast brings insight into the prion

The use of yeast systems to study the propagation of prions and amyloids has emerged as a crucial aspect of the global endeavor to understand those mechanisms. Yeast prion systems are intrinsically multi-scale: the molecular chemical processes are indeed coupled to the cellular processes of cell growth and division to influence phenotypical traits, observable at the scale of colonies. We introduce a novel modeling framework to tackle this difficulty using impulsive differential equations. We apply this approach to the [PSI+] yeast prion, which associated with the misconformation and aggregation of Sup35. We build a model that reproduces and unifies previously conflicting experimental observations on [PSI+] and thus sheds light onto characteristics of the intracellular molecular processes driving aggregate replication. In particular our model uncovers a kinetic barrier for aggregate replication at low densities, meaning the change between prion or prion-free phenotype is a bi-stable transition. This result is based on the study of prion curing experiments, as well as the phenomenon of colony sectoring, a phenotype which is often ignored in experimental assays and has never been modeled. Furthermore, our results provide further insight into the effect of guanidine hydrochloride (GdnHCl) on Sup35 aggregates. To qualitatively reproduce the GdnHCl curing experiment, aggregate replication must not be completely inhibited, which suggests the existence of a mechanism different than Hsp104-mediated fragmentation. Those results are promising for further development of the [PSI+] model, but also for extending the use of this novel framework to other yeast prion or amyloid systems. Author summaryIn the study of yeast prions, mathematical modeling is a powerful tool, in particular when it comes to facing the difficulties of multi-scale systems. In this study, we introduce a mathematical framework for investigating this problem in a unifying way. We focus on the yeast prion [PSI+] and present a simple molecular scheme for prion replication and a model of yeast budding. In order to qualitatively reproduce experiments, we need to introduce a non-linear mechanism in the molecular rates. This transforms the intracellular system into a bi-stable switch and allows for curing to occur, which is a crucial phenomenon for the study of yeast prions. To the best of our knowledge, no model in the literature includes such a mechanism, at least not explicitly. We also describe the GdnHCl curing experiment, and the propagon counting procedure. Reproducing this result requires challenging hypotheses that are commonly accepted, and our interpretation gives a new perspective on the concept of propagon. This study may be considered as a good example of how mathematical modeling can bring valuable insight into biological concepts and observations.

systems biology↗