Biology subjects

Lorenzo, A.

Publications and source records attributed to Lorenzo, A..

3 recordsLinked to original sources

bioRxiv2025

Molecular basis of polyadenylated RNA fate determination in the nucleus

Eukaryotic genomes generate a plethora of polyadenylated (pA+) RNAs1,2, that are packaged into ribonucleoprotein particles (RNPs). To ensure faithful gene expression, functional pA+ RNPs, including protein-coding RNPs, are exported to the cytoplasm, while transcripts within non-functional pA+ RNPs are degraded in the nucleus1-4. How cells distinguish these opposing fates remains unknown. The DExD-box ATPase UAP56/DDX39B is a central component of functional pA+ RNPs, promoting their docking to the nuclear pore complex (NPC)-anchored transcription and export complex 2 (TREX-2) (ref.5,6), which triggers transcript release from UAP56 to facilitate export (ref.7,8). Here, we uncover that the Poly(A) tail exosome targeting (PAXT) connection9 harbors its own TREX-2-like module, which releases pA+ RNAs from UAP56 for decay by the nuclear exosome. The core of this module consists of a LENG8-PCID2-SEM1 (LENG8-PS) trimer, which we show is structurally and functionally equivalent to the central GANP-PCID2-SEM1 (GANP-PS) trimer of TREX-2. Mutagenesis and transcriptomic data demonstrate that the nuclear fate of pA+ RNPs is governed by the contending actions of nucleoplasmic PAXT and NPC-associated TREX-2, which interpret RNA-bound UAP56 as a signal for RNA decay or export, respectively. As RNA targets of PAXT are generally short and intron-poor, we propose an overall model for pA+ RNP fate determination, whereby the distinct sub-nuclear localizations of PAXT and TREX-2 govern the degradation of short non-functional pA+ RNAs while allowing export of their longer and functional counterparts.

molecular biology↗

bioRxiv2021

Aβ promotes amyloidogenic processing of APP through a Go/Gβγ signaling

Alzheimers disease (AD) is characterized by a cognitive impairment associated to amyloid beta (A{beta}) aggregation and deposition in the brain. A{beta} is generated by sequential cleavage of the amyloid precursor protein (APP) by {beta}-site APP cleaving enzyme 1 (BACE1) and {gamma}-secretase complex. The mechanisms that underlie exacerbated production of A{beta}, favoring its deposition in the brain, is largely unknown. In vitro studies have shown that A{beta} aggregates trigger enhanced production of A{beta} by a yet non described mechanism. Here, we show that in different cell types, including human neurons derived from induced pluripotent stem cells (iPSC), oligomers and fibrils of A{beta} enhance the convergence and interaction of APP and BACE1 in endosomal compartments. We demonstrated a key role of A{beta}-APP/Go/G{beta}{gamma} signaling on the amyloidogenic processing of APP. We show that APP mutants with impaired capacity to bind A{beta} or to activate Go protein, are unable to exacerbate APP and BACE1 colocalization in the presence of A{beta}. Moreover, pharmacological inhibition of G{beta}{gamma} subunits signaling with gallein, abrogate A{beta}-dependent interaction of APP and BACE1 in endosomes preventing {beta}-processing of APP. Collectively, these findings uncover a feed-forward mechanism of amyloidogenesis that might contribute to A{beta} pathology in early stages of AD and suggest that gallein might have clinical relevance.

neuroscience↗

bioRxiv2018

Long-read amplicon denoising

Long-read next generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies. Called \"amplicon denoising\", this problem has been extensively studied for short-read sequencing technologies, but current solutions do not appear to generalize well to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads (here ~2.6kb) and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower.\n\nOn one real dataset with ground truth, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method.\n\nFast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD) are implemented purely in the Julia scientific computing language, and are hereby released along with a complete toolkit of functions that allow long-read amplicon sequence analysis pipelines to be constructed in pure Julia. Further, we make available a webserver to dramatically simplify the processing of long-read PacBio sequences.

bioinformatics↗