Biology subjects

Shasha, D.

Publications and source records attributed to Shasha, D..

7 recordsLinked to original sources

bioRxiv2025

Hi-C for genome-wide detection of enhancer-hijacking rearrangements in routine lymphoid cancer biopsies

Standard techniques for detecting genomic rearrangements in formalin-fixed paraffin-embedded (FFPE) biopsies have important limitations. We performed FFPE-compatible Hi-C on 44 clinical biopsies comprising large B-cell lymphomas (n=18), plasma cell neoplasms (n=14), and other diverse lymphoid cancers, identifying consistent topological differences between malignant B cell and plasma cell states. Hi-C detected expected oncogene rearrangements at high concordance with fluorescence in-situ hybridization (FISH) and supported enhancer-hijacking in recurrent rearrangements of BCL2, CCND1, and MYC, plus unanticipated variants involving homologous loci. Hi-C identified unanticipated non-coding rearrangements involving PD-1 ligand genes and other loci of potential therapeutic relevance, distinguished between functionally divergent classes of BCL6 rearrangements, and provided topological information supporting the interpretation of atypical MYC rearrangements. In biopsies lacking MYC-activating rearrangements, Hi-C revealed differential interactions with functionally-validated disease-specific native MYC locus enhancers. FFPE-compatible Hi-C detects oncogene rearrangements and their topological consequences at genome-wide scale, finding clinically-relevant drivers that are missed by standard approaches.

genomics↗

bioRxiv2023

Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert

The best ideotypes are under mounting pressure due to increased aridity in many parts of the world. Understanding the conserved molecular mechanisms that evolve in wild plant species adapted to harsh environments is crucial in developing new strategies for sustainable agriculture. Yet our knowledge of such mechanisms in wild species is scant, particularly in extreme environments. We performed metabolic pathway reconstruction using transcriptome information from 32 Atacama plant species and phylogenetically related plant species that do not live in Atacama (Sister species). We analyzed pathway and reaction enrichment to understand the biochemical commonalities and differences of wild Atacama plant species. To gain insights into the mechanisms that ensure plant survival, we compared expressed gene isoform numbers and gene expression patterns between the annotated biochemical reactions from 32 Atacama and Sister species. We found significant biochemical convergences in primary and secondary metabolism characterized by reactions enriched in at least 50% of the Atacama species across major plant phylogenetic lineages. Analysis of the annotation indicated potential advantages against drought, salinity, high solar irradiance, and nitrogen starvation. These findings suggest that the adaptation in the Atacama Desert may result in part from shared genetic legacies governing the expression of key metabolic pathways to face harsh environmental conditions. Enriched reactions corresponded to ubiquitous compounds common to extreme and agronomic species and were congruent with our previous metabolomic analyses in these Atacama species. Hence, genes underlying these adaptive traits offer promising candidates for improving abiotic stress resilience in crop species.

plant biology↗

bioRxiv2023

Candida auris evades innate immunity by using metabolic strategies to escape and kill macrophages while avoiding antimicrobial inflammation

Candida auris causes life-threatening, drug-resistant infections. In addition to drug resistance, therapeutic innovation is hindered by our limited knowledge of the mechanisms used by C. auris to evade immunity and establish infection. Here we show that C. auris escapes phagocytic containment and kills macrophages, and demonstrate that the mechanisms rely on metabolic regulation. We found that C. auris-infected macrophages undergo immunometabolic reprogramming and increase glycolysis but this does not lead to the expected antimicrobial responses, as macrophages fail to activate IL-1{beta} cytokine and curb C. auris growth. Further analysis showed that C. auris relies on its own metabolic capacity to egress from macrophages, cause macrophage metabolic stress and cell death, and establish infection in vivo. We identified a transcriptional regulator of C. auris metabolism and macrophage evasion, and further show that, contrary to several other pathogens, C. auris-induced macrophage metabolic dysfunction and death fail to activate the NLRP3 inflammasome. Consequently, inflammasome-dependent antimicrobial responses remain inhibited throughout infection. Our findings establish a pivotal role for metabolic regulation in enabling C. auris to eliminate macrophages while remaining immunologically silent to ensure its own survival.

microbiology↗

bioRxiv2021

Pheniqs 2.0: accurate, high performance Bayesian decoding and confidence estimation for combinatorial barcode indexing

BackgroundSystems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation. ResultsWe developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of >11 billion reads in 1h15m using <50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility. ConclusionsWe introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

bioinformatics↗

bioRxiv2021

Analysis of phloem trajectory links tissue maturation to cell specialization.

The mechanisms that allow cells in the plant meristem to coordinate tissue-wide maturation gradients with specialized cell networks are critical for indeterminate growth. Here, we reconstructed the protophloem developmental trajectory of 19 cells from cell birth to terminal differentiation at single cell resolution in the Arabidopsis root. We found that cellular specification is mediated near the stem cell niche by PHLOEM EARLY DOF (PEAR) transcription factors. However, the PEAR dependent differentiation program is repressed by a broad gradient of PLETHORA (PLT) transcription factors, which directly inhibit PEARs own direct target ALTERED PHLOEM DEVELOPMENT (APL). The dissipation of PLT gradient around 7 cells from the stem cell activates APL expression, and a subsequent transitional network that results in a "seesaw" pattern of mutual inhibition over developmental time. Together, we provide a mechanistic understanding of how morphogen-like maturation gradients interface with cell-type specific transcriptional regulators to stage cellular differentiation.

plant biology↗

bioRxiv2017

Pheniqs: Fast and flexible quality-aware sequence demultiplexing

1MotivationOutput from high throughput sequencing instruments often exceeds what is necessary to assay a single sample. To better utilize this capacity, multiple samples are independently tagged with a unique \"barcode\" sequence and are then pooled, or \"multiplexed\", and sequenced together. Classifying, or \"demultiplexing\", the reads involves decoding the barcode sequence. Although instruments estimate the probability of incorrectly calling each nucleobase, available demultiplexers do not consult those estimates or report classification error probabilities.\n\nResultsWe present Pheniqs, a fast and flexible sequence demultiplexer and quality analyzer. In addition to providing an efficient implementation of the widespread minimum distance decoder, Pheniqs introduces a novel Phred-adjusted maximum likelihood decoder that consults base calling quality scores and estimates the probability of a barcode decoding error. Setting an upper bound on the permissible error provides an intuitive way to control demultiplexing confidence and directly influence precision and recall. Pheniqs supports FASTQ and multiple Sequence Alignment/Map formats and uses auxiliary SAM tags to report both library classification and demultiplexing error probability. Evaluation on both real and semi-synthetic data indicates that Pheniqs is faster than existing demultiplexers, substantially when demultiplexing longer reads, and achieves greater accuracy by correctly reflecting quality measurements.\n\nAvailability and ImplementationImplemented in multithreaded C++ and available under the terms of the AGPL-3.0 license agreement at http://github.com/biosails/pheniqs. Manual and examples are available at http://biosails.github.io/pheniqs.

bioinformatics↗

bioRxiv2017

An Adaptive Geometric Search Algorithm for Macromolecular Scaffold Selection

A wide variety of protein and peptidomimetic design tasks require matching functional three-dimensional motifs to potential oligomeric scaffolds. Enzyme design, for example, aims to graft active-site patterns typically consisting of 3 to 15 residues onto new protein surfaces. Identifying suitable proteins capable of scaffolding such active-site engraftment requires costly searches to identify protein folds that can provide the correct positioning of side chains to host the desired active site. Other examples of biodesign tasks that require simpler fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications the speed and scaling of geometric search limits downstream design to small patterns. Here we present an adaptive algorithm to searching for side chain take-off angles compatible with an arbitrarily specified functional pattern that enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided but our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive-geometric-search-for-protein-design).

bioengineering↗