Biology subjects

Chatzipli, A.

Publications and source records attributed to Chatzipli, A..

6 recordsLinked to original sources

bioRxiv2026

Hybrid transcriptome assembly and annotation of Japanese macaque prefrontal cortex

Japanese macaque (Macaca fuscata) is used in biomedical and neurobiology research, yet transcriptomic resources for the brain are limited. We present a hybrid RNA sequencing dataset and a prefrontal cortex transcriptome assembly from two healthy 6-year-old animals. Short-read Illumina ({approx}70 million paired-end reads per sample) and long-read Oxford Nanopore direct RNA sequencing ({approx}2.5 million reads per sample) were combined. Reads were quality controlled, aligned to the macFus_1.0 reference genome, and assembled with StringTie2. Transcripts were annotated using Trinotate and eggNOG-mapper, and open reading frames were predicted with TransDecoder. The released data package includes raw reads (NCBI SRA BioProject PRJNA1295993), transcript sequences and structural annotation files, predicted coding sequences and proteins, functional annotation tables, and transcript abundance estimates (TPM). Technical validation includes read-level QC and protein-level comparisons to expressed gene sets from human, rhesus macaque and chimpanzee prefrontal cortex. These resources enable reuse for transcript-level expression studies, isoform characterization and comparative primate neurogenomics.

neuroscience↗

bioRxiv2025

Multi-omic analysis of the ciliogenic transcription factor RFX3 reveals a role in promoting activity-dependent responses via enhancing CREB binding in human neurons

Heterozygous loss-of-function (LoF) variants in RFX3, a transcription factor known to play key roles in ciliogenesis, result in autism spectrum disorder (ASD) and neurodevelopmental delay. RFX binding motifs are also enriched upstream of genes found to be commonly dysregulated in transcriptomic analyses of brain tissue from individuals with idiopathic ASD. Still, the precise functions of RFX3 in the human brain is unknown. Here, we studied the impact of RFX3 deficiency using human iPSC-derived neurons and forebrain organoids. Biallelic loss of RFX3 disrupted ciliary gene expression and delayed neuronal differentiation, while monoallelic loss of RFX3 did not. Instead, transcriptomic and DNA binding analyses demonstrated that monoallelic RFX3 loss disrupted synaptic target gene expression and diminished neuronal activity-dependent gene expression. RFX3 binding sites co-localized with CREB binding sites near activity-dependent genes, and RFX3 deficiency led to decreased CREB binding and impaired induction of CREB targets in response to neuronal depolarization. This study demonstrates a novel role of the ASD-associated gene RFX3 in shaping neuronal synaptic development and plasticity.

neuroscience↗

bioRxiv2023

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer PDXs

AO_SCPLOWBSTRACTC_SCPLOWPatient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. We characterised 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic level and measured their response to cetuximab, an EGFR inhibitor in clinical use for metastatic colorectal cancer. After assessing PDXs quality, stability, and molecular concordance with publicly available patient cohorts, we trained, interpreted, and validated an integrated ensemble classifier (CeSta) which takes in input the PDXs multi-omic characterisation and predicts their sensitivity to cetuximab treatment (AUROC > 0.9). Our study shows that large PDX collections can be used to train accurate, interpretable models of drug sensitivity, which 1) better recapitulate patient-derived therapeutic biomarkers than other models trained on CCL data, 2) can be robustly validated across independent PDX cohorts, and 3) can be used for the development of novel therapeutic biomarkers.

bioinformatics↗

bioRxiv2022

Genomic patterns of malignant peripheral nerve sheath tumour (MPNST) evolution correlate with clinical outcome and are detectable in cell-free DNA

Malignant peripheral nerve sheath tumour (MPNST) is an aggressive soft-tissue sarcoma that arises in peripheral nerves. MPNST occurs either sporadically or in people with neurofibromatosis type 1 (NF1), a common cancer predisposition syndrome caused by germline pathogenic variants in NF1. Although MPNST is the most common cause of death and morbidity for individuals with NF1, the molecular underpinnings of MPNST pathogenesis remain unclear. Here, we report the analysis of whole-genome sequencing, multi-regional exome sequencing, transcriptomic and methylation profiling data for 95 MPNSTs and precursor lesions (64 NF1-related; 31 sporadic) from 77 individuals. Early events in tumour evolution include biallelic inactivation of NF1 followed by inactivation of CDKN2A and in some cases also TP53 and polycomb repressive complex 2 (PRC2) genes. Subsequently, both sporadic and NF1-related MPNSTs acquire a high burden of somatic copy number alterations (SCNAs). Our analysis revealed distinct pathways of tumour evolution and immune infiltration associated with inactivation of PRC2 genes and H3K27 trimethylation (H3K27me3) status. Tumours with loss of H3K27me3 evolve through extensive chromosomal losses with retention of chromosome 8 heterozygosity followed by whole genome doubling and chromosome 8 amplification. These tumours show lower levels of immune cell infiltration with low cytotoxic activity and low expression of immune checkpoints. In contrast, tumours with retention of H3K27me3 evolve through extensive genomic instability in the absence of recurrent alterations and exhibit an immune cell-rich phenotype. Specific SCNAs detected in both tumour samples and cell-free DNA (cfDNA) act as a surrogate for loss of H3K27me3 and immune infiltration, and predict prognosis. Our results suggest that SCNA profiling of tumour or cfDNA could serve as a biomarker for early diagnosis and to stratify patients into prognostic and treatment-related subgroups.

cancer biology↗

bioRxiv2019

APOBEC3B-dependent kataegis and TREX1-driven chromothripsis in telomere crisis

Chromothripsis and kataegis are frequently observed in cancer and can arise from telomere crisis, a period of genome instability during tumorigenesis when depletion of the telomere reserve generates unstable dicentric chromosomes1-5. Here we report on the mechanism underlying chromothripsis and kataegis using an in vitro telomere crisis model. We show that the cytoplasmic exonuclease TREX1, which promotes the resolution of dicentric chromosomes4, plays a prominent role in chromothriptic fragmentation. In absence of TREX1, the genome alterations induced by telomere crisis primarily involve Breakage-Fusion-Bridge cycles and simple genome rearrangements rather than chromothripsis. Furthermore, we show that the kataegis observed at chromothriptic breakpoints is the consequence of cytosine deamination by APOBEC3B. In addition, APOBEC3B increased the frequency of chromothriptic fragmentation, possibly due to strand breakage after cytosine deamination. These data reveal that chromothripsis and kataegis arise from a combination of nucleolytic processing by TREX1 and cytosine editing by APOBEC3B.

molecular biology↗

bioRxiv2019

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗