Biology subjects

Campanelli, A.

Publications and source records attributed to Campanelli, A..

2 recordsLinked to original sources

bioRxiv2024

Tumor-associated macrophages enhance tumor innervation and spinal cord repair

AO_SCPLOWBSTRACTC_SCPLOWTumor-associated macrophages (TAM) enhance cancer progression by promoting angiogenesis, extracellular matrix (ECM) remodeling, and immune suppression. Nerve infiltration is a hallmark of various cancers and is known to directly contribute to tumor growth. However, the role of TAM in promoting intratumoral nerve growth remains poorly understood. In this study, we demonstrate that TAM expressed a distinct "neural growth" gene signature. TAM actively enhance neural growth within tumors and directly promote neurites outgrowth. We identify secreted phosphoprotein 1 (Spp1) as a key mediator of TAM-driven neural growth activity, which triggers neuronal mTORC2 signaling. Leveraging this new neural growth function, which added to the TAM wound healing properties, we explored TAM potential to repair central nervous system. Adoptive transfer of in vitro-generated TAM in a severe complete-compressive-contusive spinal cord injury (scSCI) model, not only repaired the damaged neural parenchyma by improving tissue oxygenation, ECM remodeling, and dampening chronic inflammation, but also resulted in neural regrowth and partial functional motor recovery. Proteomic analysis and subsequent functional validation confirmed that TAM-induced spinal cord regeneration is mediated through the activation of neural mTORC2 signaling pathways. Collectively, our data unveil a previously unrecognized role of TAM in tumor innervation, neural growth, and neural tissue repair.

neuroscience↗

bioRxiv2024

Where the patterns are: repetition-aware compression for colored de Bruijn graphs

We describe lossless compressed data structures for the colored de Bruijn graph (or, c-dBG). Given a collection of reference sequences, a c-dBG can be essentially regarded as a map from k-mers to their color sets. The color set of a k-mer is the set of all identifiers, or colors, of the references that contain the k-mer. While these maps find countless applications in computational biology (e.g., basic query, reading mapping, abundance estimation, etc.), their memory usage represents a serious challenge for large-scale sequence indexing. Our solutions leverage on the intrinsic repetitiveness of the color sets when indexing large collections of related genomes. Hence, the described algorithms factorize the color sets into patterns that repeat across the entire collection and represent these patterns once, instead of redundantly replicating their representation as would happen if the sets were encoded as atomic lists of integers. Experimental results across a range of datasets and query workloads show that these representations substantially improve over the space effectiveness of the best previous solutions (sometimes, even dramatically, yielding indexes that are smaller by an order of magnitude). Despite the space reduction, these indexes only moderately impact the efficiency of the queries compared to the fastest indexes. SoftwareThe implementation of the indexes used for all experiments in this work is written in C++17 and is available at https://github.com/jermp/fulgor.

bioinformatics↗