Biology subjects

Sabeti, P.

Publications and source records attributed to Sabeti, P..

6 recordsLinked to original sources

bioRxiv2026

Rapid Cas13a-based penA genotyping for cefixime susceptibility in Neisseria gonorrhoeae

BackgroundAntimicrobial resistance in Neisseria gonorrhoeae is an urgent public health threat. Resistance-guided therapy can assure appropriate treatment and reintroduce alternative therapeutic options by identifying genetic predictors of resistance. Mosaicism at codons 375-377 of the penA gene are associated with cefixime resistance. Rapid, field-deployable assays for predicting cefixime susceptibility are lacking. MethodsWe used a machine-learning algorithm to develop a CRISPR Cas13a-based assay to detect the absence of mosaicism at codons 375-377 of the penA gene combined with isothermal amplification in a single reaction. We integrated the assay onto a portable fluorescence-based platform. We evaluated performance using cultured isolates and compared results with PCR genotyping and phenotypic antimicrobial susceptibility testing. We also assessed feasibility of reagent lyophilization for cold-chain-independent deployment. ResultsAmong 40 N. gonorrhoeae isolates, the Cas13a penA assay demonstrated 100% concordance with PCR genotyping and 92{middle dot}5% (95% CI 79.6-98.4%) concordance with phenotypic cefixime susceptibility. Median time to detection was 12 minutes (IQR 5 minutes). The lyophilized detection system detected all 12 isolates with a median time to detection of 45{middle dot}0 minutes (IQR 40-45) compared to 45{middle dot}0 minutes (IQR 35-50) for the positive aqueous control, although peak fluorescence was higher for the aqueous control (p<0.01). ConclusionThe Cas13a assay was rapid and demonstrated strong correlation with genotypic and phenotypic cefixime susceptibility in N. gonorrhoeae, while a lyophilized assay retained functionality.

bioengineering↗

bioRxiv2026

Modular mRNA LNP design integrates RNA, lipid, and antigen engineering for protective vaccination

mRNA-lipid nanoparticle (LNP) vaccines are programmable, multi-component systems in which immune outcomes emerge from coupled control of nanoparticle chemistry, RNA regulatory architecture, and antigen design. Here we establish an integrated engineering framework that quantitatively maps how ionizable lipid identity, untranslated region (UTR) configuration, and 5' cap structure shape innate activation landscapes and thereby tune the magnitude, cellular distribution, and polarization of adaptive immunity. Benchmarking three ionizable lipids shows that lipid chemistry imprints distinct cytokine and chemokine milieus during dendritic cell-T cell priming that mirror downstream T cell activation phenotypes, identifying lipid structure as a determinant of pathway-selective activation; notably, our first-in-study lipid exhibits benchmark-comparable immunostimulatory profiles, supporting further translational evaluation. Using Crimean-Congo hemorrhagic fever virus (CCHFV) as a model high-consequence pathogen, we show that UTRs act as modular regulatory elements that redirect cytokine outputs, tuning effector versus proliferative programs and expanding helper polarization in an antigen-dependent manner. Cap structure functions primarily as a quantitative gain control, scaling cellular and humoral response magnitude without overriding antigen-defined polarization. Within this optimized platform, antigen architecture defines functional constraints on protection: structural modifications reshape immune hierarchies and antibody quality. Integrating these design axes yields an optimized mRNA-LNP vaccine encoding the CCHFV secreted glycoprotein complex (sGCs) that achieves 90% protection in an immunosuppressed murine lethal-challenge model with minimal clinical signs. Together, these data define generalizable design principles for rational, multi-parameter optimization of mRNA-LNP vaccines.

microbiology↗

bioRxiv2024

Aging disrupts spatiotemporal coordination in the cycling ovary

Throughout the female reproductive lifespan, the ovary completes hundreds of cycles of follicle development, ovulation, and tissue regeneration1-3. These processes rely on the precisely coordinated intricate multicellular interactions across time and space4. How aging disrupts these interactions, leading to an overall decline in reproductive and endocrine functions, remains understudied. To understand the multicellular dynamics that underlie ovarian function and their changes with age, here we use Slide-seq, a near-cellular spatial transcriptomics method, to profile 22 mouse ovaries across the reproductive cycle and chronological age, representing 610,620 near-cellular spots across 69 spatial transcriptomic profiles5,6. We develop a segmentation analysis to identify spatial niches that capture different states of folliculogenesis from static snapshots in situ, allowing us to examine the multicellular dynamics of 358 oocytes, 668 follicles, and 236 corpora lutea. We find that aging disrupts both the spatial organization and temporal coordination of folliculogenesis before the cessation of cycling, which may contribute to the dysregulation of hormone production and signaling. These disruptions are marked by altered immune cell dynamics, inflammatory signaling, and global tissue disorganization that impair the cyclic remodeling required for ovarian function. Our findings reveal how multicellular niches orchestrate ovarian function and demonstrate how age-related breakdown of tissue organization across time and space precedes reproductive decline.

genomics↗

bioRxiv2021

Multiplexed detection of bacterial nucleic acids using Cas13 in droplet microarrays

Rapid and accurate diagnosis of infections is fundamental to individual patient care and public health management. Nucleic acid detection methods are critical to this effort, but are limited either in the breadth of pathogens targeted or by the expertise and infrastructure required. We present here a high-throughput system that enables rapid identification of bacterial pathogens, bCARMEN, which utilizes: (1) modular CRISPR-Cas13-based nucleic acid detection with enhanced sensitivity and specificity; and (2) a droplet microfluidic system that enables thousands of simultaneous, spatially multiplexed detection reactions at nanoliter volumes; and (3) a novel pre-amplification strategy that further enhances sensitivity and specificity. We demonstrate bCARMEN is capable of detecting and discriminating 52 clinically relevant bacterial species and several key antibiotic resistance genes. We further develop a proof of principle system for use with stabilized reagents and a simple workflow with optical readout using a cell phone camera, opening up the possibility of a rapid point-of-care multiplexed bacterial pathogen identification and antibiotic susceptibility testing. Significance StatementIn this paper, we use a novel primer design method combined with droplet-based CRISPR Cas13 detection to distinguish 52 clinically relevant bacterial pathogens in a single assay. We also apply the method to detect and distinguish a panel of major antibiotic resistance genes, which is of critical importance in this era of rising antibiotic resistance. Finally, we make key advances towards making our diagnostic assay deployable at the point-of-care, with a simplified emulsion-free assay process that uses mobile phone camera for detection and reduces infrastructure/skilled labor requirements.

microbiology↗

bioRxiv2020

Enhanced virus detection and metagenomic sequencing in patients with meningitis and encephalitis

Meningitis and encephalitis are leading causes of central nervous system (CNS) disease and often result in severe neurological compromise or death. Traditional diagnostic workflows largely rely on pathogen-specific diagnostic tests, sometimes over days to weeks. Metagenomic next-generation sequencing (mNGS) is a high-throughput platform that profiles all nucleic acid in a sample. We prospectively enrolled 68 patients from New England with known or suspected CNS infection and performed mNGS from both RNA and DNA to identify potential pathogens. Using a computational metagenomic classification pipeline based on KrakenUniq and BLAST, we detected pathogen nucleic acid in cerebrospinal fluid (CSF) from 22 subjects. This included some pathogens traditionally diagnosed by serology or not typically identified in CSF, including three transmitted by Ixodes scapularis ticks (Powassan virus, Borrelia burgdorferi, Anaplasma phagocytophilum). Among 24 subjects with no clinical diagnosis, we detected enterovirus in two subjects and Epstein Barr virus in one subject. We also evaluated two methods to enhance detection of viral nucleic acid, hybrid capture and methylated DNA depletion. Hybrid capture nearly universally increased viral read recovery. Although results for methylated DNA depletion were mixed, it allowed detection of varicella zoster virus DNA in two samples that were negative by standard mNGS. Overall, mNGS is a promising approach that can test for multiple pathogens simultaneously, with similar efficacy to pathogen-specific tests, and can uncover geographically relevant infectious CNS disease, such as tick-borne infections in New England. With further laboratory and computational enhancements, mNGS may become a mainstay of workup for encephalitis and meningitis. ImportanceMeningitis and encephalitis are leading global causes of central nervous system (CNS) disability and mortality. Current diagnostic workflows remain inefficient, requiring costly pathogen-specific assays and sometimes invasive surgical procedures. Despite intensive diagnostic efforts, 40-60% of people with meningitis or encephalitis have no clear cause of their CNS disease identified. As diagnostic uncertainty often leads to costly inappropriate therapies, the need for novel pathogen detection methods is paramount. Metagenomic next-generation sequencing (mNGS) offers the unique opportunity to circumvent these challenges using unbiased laboratory and computational methods. Here, we performed comprehensive mNGS from 68 patients with suspected CNS infection, and define enhanced methods to improve the detection of CNS pathogens, including those not traditionally identified in the CNS by nucleic acid detection. Overall, our work helps elucidate how mNGS can become a mainstay in the diagnostic toolkit for CNS infections.

neuroscience↗

bioRxiv2017

Genome-wide regulatory model from MPRA data predicts functional regions, eQTLs, and GWAS hits

Massively-parallel reporter assays (MPRA) enable unprecedented opportunities to test for regulatory activity of thousands of regulatory sequences. However, MPRA only assay a subset of the genome thus limiting their applicability for genome-wide functional annotations. To overcome this limitation, we have used existing MPRA datasets to train a machine learning model that uses DNA sequence information, regulatory motif annotations, evolutionary conservation, and epigenomic information to predict genomic regions that show enhancer activity when tested in MPRA assays. We used the resulting model to generate global predictions of regulatory activity at single-nucleotide resolution across 14 million common variants. We find that genetic variants with stronger predicted regulatory activity show significantly lower minor allele frequency, indicative of evolutionary selection within the human population. They also show higher over-lap with eQTL annotations across multiple tissues relative to the background SNPs, indicating that their perturbations in vivo more frequently result in changes in gene expression. In addition, they are more frequently associated with trait-associated SNPs from genome-wide association studies (GWAS), enabling us to prioritize genetic variants that are more likely to be causal based on their predicted regulatory activity. Lastly, we use our model to compare MPRA inferences across cell types and platforms and to prioritize the assays most predictive of MPRA assay results, including cell-dependent DNase hypersensitivity sites and transcription factors known to be active in the tested cell types. Our results indicate that high-throughput testing of thousands of putative regions, coupled with regulatory predictions across millions of sites, presents a powerful strategy for systematic annotation of genomic regions and genetic variants.

bioinformatics↗