Biology subjects

Saeed, K.

Publications and source records attributed to Saeed, K..

4 recordsLinked to original sources

bioRxiv2024

KlebPhaCol: A community-driven resource for Klebsiella research identified a novel gut phage order associated with the human gut

The growing threat of multidrug-resistant Klebsiella pneumoniae, coupled with its role in gut colonisation, has intensified the search for new treatments, including bacteriophage therapy. Despite increasing documentation of Klebsiella-targeting phages, clinical applications remain limited, with key phage-bacteria interactions still poorly understood. A major obstacle is fragmented access to well-characterised phage-bacteria pairings, restricting the collective advancement of therapeutic and mechanistic insights. To address this gap, we created the Klebsiella Phage Collection (KlebPhaCol), an open-source resource comprising 53 phages and 74 Klebsiella isolates, all fully characterised. These phages span five families - including a novel order, Felixvirales, associated with the human gut - and target 27 sequence types (including ST258, ST11, ST14) and 28 capsular-locus types (including KL1 and KL2), across six Klebsiella species. Freely accessible at www.klebphacol.org, KlebPhaCol invites the scientific community to both use and contribute to this resource, fostering collaborative research and a deeper understanding of Klebsiella-phage interactions beyond therapeutic use.

microbiology↗

bioRxiv2024

A benchmark comparison of CRISPRn guide-RNA design algorithms and generation of small single and dual-targeting libraries to boost screening efficiency

Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale.

molecular biology↗

bioRxiv2022

Single-cell functional genomics of natural killer cell evasion in blood cancers

Natural killer (NK) cells are emerging as a promising therapeutic option in cancer. To better understand how cancer cells evade NK cells, we studied interacting NK and blood cancer cells using single-cell and genome-scale functional genomics screens. At single-cell resolution, interaction of NK and cancer cells induced distinct activation states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to more resistant B-lymphoid cancers. CRISPR screens uncovered cancer cell-intrinsic genes driving sensitivity and resistance, including antigen presentation and death receptor signaling mediators, adhesion molecules, protein fucosylation genes, and transcriptional regulators. CRISPR screens with a single-cell transcriptomic readout revealed how these cancer cell genes influenced the gene expression landscape of both cell types, including regulation of activation states in both cancer and NK cells by IFN{gamma} signaling. Our findings provide a resource for rational design of NK cell-based therapies in blood cancers. HIGHLIGHTSO_LITranscriptomic states of interacting NK cells and cancer cells depend on cancer cell lineage C_LIO_LIMolecular correlates of increased sensitivity of myeloid compared to B-lymphoid cancers include activating receptor ligands NCR3LG1, PVR, and ULBP1 C_LIO_LINew regulators of NK cell resistance from 12 genome-scale CRISPR screens include blood cancer-specific regulators SELPLG, SPN, and MYB C_LIO_LISingle-cell transcriptomics CRISPR screens targeting 65 genome-wide screen hits identify MHC-I, IFNy, and NF-{kappa}B regulation as underlying mechanisms C_LI

immunology↗

bioRxiv2022

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance. We applied ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells. The pre-resistant phenotypes were defined by cell cycle and proteostatic features, reflecting the traits enriched in the upcoming subclonal selection. Furthermore, DNA repair deficiency rendered cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leveraged the pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

cancer biology↗