Biology subjects

Martinelli, A.

Publications and source records attributed to Martinelli, A..

At least 19 recordsLinked to original sources

bioRxiv2026

WGCNA+: AI-powered WGCNA for Integration of Multi-Omics Data

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results: To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions: WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation: WGCNA+ and LASAGNA are implemented in R language for statistical computing, version[≥]3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions (https://github.com/bigomics/WGCNAplus; https://github.com/bigomics/lasagna)

bioinformatics↗

bioRxiv2026

The RNA cargo of plasma-derived extracellular vesicles in mCRPC patients captures cancer cells and tumor microenvironment signals

Circulating analytes in cancer patients capture tumor-related signals. We profiled matched extracellular vesicle (EV) total RNA and cell-free DNA (cfDNA) from the same plasma aliquots of chemo-naive metastatic castration-resistant prostate cancer (mCRPC) patients treated with Enzalutamide (n=54 patients, n=119 longitudinal samples; NCT06981377) at four Italian clinical centers and interrogated data from >10,000 cancer patients' and healthy individuals' samples. Transcript integrity analysis identified coding and non-coding species with fragmentation patterns varying across RNA biotypes. EV-RNA data deconvolution revealed signal from immune populations, including fractions classified as CD4+ T cells, whose abundance increased with disease progression in plasma EVs and mCRPC tissues. By leveraging tissue data-informed mining, we established a novel prostate cancer-related EV-RNA signature that resulted in an independent predictor of poor prognosis and captured tumor microenvironment-derived signals. Integrating EV-RNA and ctDNA information improved patient stratification for progression-free survival. These findings suggest a multifaceted role for plasma EVs as a source of cancer biomarkers.

cancer biology↗

bioRxiv2026

Spatial single-cell proteomics defines multicellular niches in the primary prostate cancer microenvironment

Prostate cancer displays substantial clinical and histopathological heterogeneity which is not fully captured by conventional Gleason grading. To resolve the spatial and phenotypic complexity of the prostate tumor microenvironment, we performed imaging mass cytometry using a prostate-tailored 34-plex antibody panel on a clinically annotated tissue microarray cohort of 195 patients of primary stage disease after radical prostatectomy (523 regions of interest; 2.19 million cells). We identified 34 distinct cell types spanning epithelial, endothelial, stromal and immune compartments, and further organized into 18 epithelial-dominated, cancer associated fibroblast-dominated, and immune-rich spatial niches. Within the epithelial compartment, we detected an ERGp53 luminal population whose abundance is independently associated with poor overall and progression-free survival. In the stroma, we defined extracellular matrix remodeling-related cancer associated fibroblast and smooth muscle cell lineages, including a periglandular CD105high niche with strong stromal-immune connectivity that is selectively associated with worse clinical outcome. Finally, cumulative immune niche burden correlated with histological inflammation and stratifies for worse patient survival. Together, these data provide a spatially resolved single-cell atlas of primary PCa and reveal stromal-immune-epithelial niches with prognostic relevance beyond Gleason grade.

cancer biology↗

bioRxiv2026

Brain Age in Conduct Disorder: A Mega-Analysis of the ENIGMA Antisocial Behavior Working Group

Conduct disorder (CD) is the leading global cause of mental health burden in children and adolescents and has recently been hypothesized to be a neurodevelopmental disorder. Although prior research has identified neuroanatomical differences associated with CD, it remains unclear whether these differences reflect atypical brain development. Here, we investigated the difference between an individuals brain age and chronological age as a proxy for variations in brain maturation. Using a pretrained model, we estimated brain age from structural neuroimaging data obtained from 1,119 youth with CD and 1,183 typically developing controls across 14 international cohorts participating in the ENIGMA-Antisocial Behavior Working Group. Youth with CD exhibited a statistically robust but small acceleration in brain age compared to typically developing youth (around 0.50 years), which was restricted to the adolescence-onset subtype of the disorder. Our large-scale, coordinated analysis provides the first evidence of accelerated neurodevelopment as a potential mechanism underlying CD.

neuroscience↗

bioRxiv2025

PLAID: ultrafast single-sample gene set enrichment scoring

MotivationIn recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples, rather than across pooled samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling identification of unique molecular profiles based on gene set and pathway activity in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. ResultsTo address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. A distinctive feature of PLAID is its integration of the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. Availability and ImplementationPLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid

bioinformatics↗

bioRxiv2024

NPmatch: Latent Batch Effects Correction of Omics data by Nearest-Pair Matching

MotivationBatch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPmatch, a nearest-neighbor matching-based method that adjusts BEs satisfactorily and outperforms current methods in a wide range of datasets. ResultsWe assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPmatch demonstrates overall superior performance in correcting for BEs while preserving biological differences than existing methods. Altogether, our method proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant. Data availability and implementationNPmatch is freely available on Github (https://github.com/bigomics/NPmatch) and on Omics Playground (https://bigomics.ch/omics-playground). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus (GEO) repository.

bioinformatics↗

bioRxiv2024

Resilience and vulnerability of speech neural tracking to early auditory deprivation

Infants are born with biological biases that favour language acquisition. One is the auditory systems ability to track the envelope of continuous speech, a pivotal feature for spoken language comprehension in adulthood. However, the extent to which neural speech tracking relies on postnatal auditory experience remains unknown. In this case-control study, we tested children with or without access to functional hearing in the first year of life after they received cochlear implants (CIs) for hearing restoration. We measured neural speech tracking in CI users with a congenital bilateral profound deafness (CD) or who acquired it later in development (AD; minimum auditory experience after birth 12 months), as well as in two groups of hearing controls listening to original (HC) or vocoded-speech (HC-v). Remarkably, neural speech tracking in children with CIs was unaffected by the absence of perinatal auditory experience. Regardless of deafness onset, CI users and HC exhibited a similar neural tracking magnitude at short timescales [~]50- 130 ms (P1TRF) of brain activity. However, this neural tracking phase (P1TRF) was delayed in CI users, and its timing depended on the age of hearing restoration. Conversely, at longer timescales [~]130-260 ms (N2TRF) of brain activity, speech tracking was substantially dampened in participants with CIs, thereby accounting for their comprehension deficits. Speech tracking in HC listening to vocoded-speech and in a phantom head-model with CIs suggested that neural processing differences between HC and CI children could not merely be explained by the degraded acoustic stimulation, nor by electrical artifacts of the implants. These findings highlight (i) the resilience of sensory components of neural speech tracking to the lack of hearing in the first year of life, (ii) the crucial role played when hearing restoration takes place in mitigating the impact of atypical auditory experience, (iii) the vulnerability of higher hierarchical levels of speech processing in CI users.

neuroscience↗

bioRxiv2024

The impact of face masks on face-to-face neural tracking of speech: auditory and visual obstacles

Face masks provide fundamental protection against the transmission of respiratory viruses but hamper communication. We estimated auditory and visual obstacles generated by face masks on communication by measuring the neural tracking of face-to-face speech. To this end, we recorded the EEG while participants were exposed to naturalistic audio-visual speech, embedded in multi-talker noise, in three contexts: (i) no-mask (audio-visual information was fully available), (ii) virtual mask (occluded lips, but intact audio), and (iii) real mask (occluded lips and degraded audio). The neural tracking of lip movements and the sound envelope of speech was measured through backward modeling, that is, by reconstructing stimulus properties from neural activity. Behaviorally, face masks increased listening -phonological-errors in speech content retrieval and perceived listening difficulty. At the neural level, we observed that the occlusion of the mouth abolished lip tracking and dampened neural tracking of the speech envelope at the earliest processing stages. Degraded acoustic information due to face mask filtering altered neural tracking at later processing stages instead. Finally, a consistent link emerged between the increment of listening perceived difficulty and the drop in reconstruction performance of speech envelope when attending to a speaker wearing a face mask. Results clearly dissociated the visual and auditory impacts of face masks on face-to-face neural tracking of speech. While face masks hampered the ability to predict and integrate audio-visual speech, the auditory filter generated by face masks impacted the neural processing stages typically associated with auditory selective attention. The link between perceived difficulty and neural tracking drop provided evidence of a major impact of face masks on the metacognitive levels subtending speech processing.

neuroscience↗

bioRxiv2023

Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions

Understanding the spatial heterogeneity of tumors and its links to disease is a cornerstone of cancer biology. Emerging spatial technologies offer unprecedented capabilities towards this goal, but several limitations hinder their clinical adoption. To date, histopathology workflows still heavily depend on hematoxylin & eosin (H&E) and serial immunohistochemistry (IHC) staining, a cumbersome and tissue-exhaustive process that yields unaligned tissue images. We propose the VirtualMultiplexer, a generative AI toolkit that translates real H&E images to matching IHC images for several markers based on contrastive learning. The VirtualMultiplexer learns from unpaired H&E and IHC images and introduces a novel multi-scale loss to ensure consistent and biologically reliable stainings. The virtually multiplexed images enabled training a Graph Transformer that simultaneously learns from the joint spatial distribution of several markers to predict clinically relevant endpoints. Our results indicate that the VirtualMultiplexer achieves rapid, robust and precise generation of virtually multiplexed imaging datasets of high staining quality that are indistinguishable from the real ones. We successfully employed transfer learning to generate realistic virtual stainings across tissue scales, patient cohorts, and cancer types with no need for model fine-tuning. Crucially, the generated images are not only realistic but also clinically relevant, as they greatly improved the prediction of different clinical endpoints across patient cohorts and cancer types, speeding up histopathology workflows and accelerating spatial biology.

bioinformatics↗

bioRxiv2023

Integrating extracellular vesicle and circulating cell-free DNA analysis on a single plasma aliquot from breast cancer patients improves the detection of HER2 positivity

BackgroundMulti-analyte liquid biopsies represents an emerging opportunity for non-invasive cancer assessment. We developed ONCE (ONe Aliquot for Circulating Elements), a novel multi-analytes liquid biopsy approach for the isolation of extracellular vesicles (EVs) and cell-free DNA (cfDNA) from a single aliquot of blood. MethodsWe assessed ONCE performance to classify HER2-positive early-stage breast cancer (BrCa) patients by combining RNA and DNA signals on n=64 healthy donors (HD) and non-metastatic BrCa patients. Specifically, we investigated EVs-derived RNA (EV-RNA) and cfDNA by next-generation sequencing (NGS) and by digital droplet PCR (ddPCR). Additionally, we utilized imaging flow cytometry to evaluate EVs as potential carriers of the HER2 protein. ResultsWestern blot analysis and immunocapture assay revealed that EVs-enriched proteins were detected at similar levels among the HER2+ and HER2- subtypes. Sequencing of cfDNA and EV-RNA from HER2- and HER2+ patients demonstrated concordance with in situ molecular analyses of matched tissues. Combined analysis of the two circulating analytes by ddPCR showed increased sensitivity in ERBB2/HER2 detection compared to single nucleic acid components. Multi-analyte liquid biopsy prediction performance was comparable to tissue-based sequencing results from TCGA. Also, we observed HER2 protein on the surface of EVs isolated from the HER2+ BrCa plasma, thus corroborating the potential relevance of studying EVs as companion analyte to cfDNA. ConclusionsThis data confirms the relevance of combining cfDNA and EV-RNA analytes for cancer assessment and supports the ONCE approach as a valuable tool for multi-analytes liquid biopsies clinical implementation.

cancer biology↗

bioRxiv2022

Early visual cortex tracks speech envelope in the absence of visual input

Neural entrainment to continuous speech is typically observed within the language network and can be modulated by both low-level acoustic features and high-level meaningful linguistic units (e.g., phonemes, phrases, and sentences). Recent evidence showed that visual cortex may entrain to speech envelope, however its putative role in the hierarchy of speech processing remains unknown. We tested blindfolded participants who listened to semantically meaningful or meaningless stories, either in quiet or embedded in multi-talker babble noise. Entrainment to speech was assessed with forward linear modeling of participants EEG activity. We investigated (1) low-level acoustic effects by contrasting neural tracking of speech presented in quiet or noise and (2) high-level linguistic effects by contrasting neural tracking to meaningful or meaningless stories. Results showed that envelope tracking was enhanced and delayed for speech embedded in noise compared to quiet. When semantic information was missing, entrainment to speech envelope was fastened and reduced. Source modeling revealed that envelope tracking engaged wide neural networks beyond the auditory cortex, including early visual cortex. Surprisingly, while no clear influence of semantic content was found, the magnitude of visual cortex entrainment was affected by low-level features. The decrease of sound SNR-level dampened visual cortex tracking, suggesting an active suppressing mechanism in challenging listening conditions. Altogether, these findings provide further evidence of a functional role of early visual cortex in the entrainment to continuous speech.

neuroscience↗

bioRxiv2020

Reanalysis of MERS, SARS and COVID-19 Infection Datasets using VirOmics Playground Reveals Common Patterns in Gene and Protein Expression

Three lethal lower respiratory tract coronavirus epidemics have occurred over the past 20 years. This coincided with major developments in genome-wide gene and protein expression analysis, resulting in a wealth of datasets in the public domain. Seven such in vitro studies were selected for comparative bioinformatic analysis through the VirOmics Playground, a visualisation and exploration platform we developed. Despite the heterogeneous nature of the data sets, several commonalities could be observed across studies and species. Differences, on the other hand, reflected not only variations between species, but also other experimental variables, such as cell lines used for the experiments, infection protocols and viral strains. The datasets analysed here are available online through our platform (https://public.bigomics.ch/app/omicsplayground_viromics).

genomics↗

bioRxiv2020

Auditory features modelling demonstrates sound envelope representation in striate cortex

The striate cortex is no longer considered exclusively visual in its function. Proofs that its activity is modulated by acoustic inputs have accrued. By employing category-based and feature modeling approaches, here we characterized V1 activity (in absence of retinal input) during the processing of natural and synthetically derived sounds. First, we showed that distinct sound categories could be dissociated by the analysis of V1 multivoxel response patterns. Hence, we assessed whether a hallmark of sound neural representations is mapped in V1. In each sound category, we modeled sound envelopes and assessed whether these were represented at the single-voxel level in the striate cortex and, as a control, in the temporal cortex. The hierarchical organization of sound categories allowed to exert control over dimensions that could spuriously lead to sound envelope V1 mapping. Variations of sound amplitude over time were successfully decoded in V1 regardless of the category class. Results confirm that the human striate cortex receives acoustic category-based input and demonstrate that V1 is a genuine locus of sound envelope representation.

neuroscience↗

bioRxiv2020

Extensive genomic and transcriptomic variation defines the chromosome-scale assembly of Haemonchus contortus, a model gastrointestinal worm

BackgroundHaemonchus contortus is a globally distributed and economically important gastrointestinal pathogen of small ruminants, and has become the key nematode model for studying anthelmintic resistance and other parasite-specific traits among a wider group of parasites including major human pathogens. Two draft genome assemblies for H. contortus were reported in 2013, however, both were highly fragmented, incomplete, and differed from one another in important respects. While the introduction of long-read sequencing has significantly increased the rate of production and contiguity of de novo genome assemblies broadly, achieving high quality genome assemblies for small, genetically diverse, outcrossing eukaryotic organisms such as H. contortus remains a significant challenge. ResultsHere, we report using PacBio long read and OpGen and 10X Genomics long-molecule methods to generate a highly contiguous 283.4 Mbp chromosome-scale genome assembly including a resolved sex chromosome. We show a remarkable pattern of almost complete conservation of chromosome content (synteny) with Caenorhabditis elegans, but almost no conservation of gene order. Long-read transcriptome sequence data has allowed us to define coordinated transcriptional regulation throughout the life cycle of the parasite, and refine our understanding of cis- and trans-splicing relative to that observed in C. elegans. Finally, we use this assembly to give a comprehensive picture of chromosome-wide genetic diversity both within a single isolate and globally. ConclusionsThe H. contortus MHco3(ISE).N1 genome assembly presented here represents the most contiguous and resolved nematode assembly outside of the Caenorhabditis genus to date, together with one of the highest-quality set of predicted gene features. These data provide a high-quality comparison for understanding the evolution and genomics of Caenorhabditis and other nematodes, and extends the experimental tractability of this model parasitic nematode in understanding pathogen biology, drug discovery and vaccine development, and important adaptive traits such as drug resistance.

genomics↗

bioRxiv2019

The dyslexia susceptibility gene KIAA0319 influences cilia length, cell migration and mechanical cell-substrate interaction

Following its association with dyslexia in multiple genetic studies, the KIAA0319 gene has been extensively investigated in different animal models but its function in neurodevelopment remains poorly understood. We developed the first cellular knockout model for KIAA0319 via CRISPR-Cas9n to investigate its role in processes suggested but not confirmed in previous studies, including cilia formation and cell migration. We found that KIAA0319 knockout increased cilia length and accelerated cell migration. Using Elastic Resonator Interference Stress Microscopy (ERISM), we detected an increase in cellular force for the knockout cells that was restored by a rescue experiment. Combining ERISM and immunostaining we show that KIAA0319 depletion reduces the number of podosomes formed by the cells. Our results suggest an involvement of KIAA0319 in cilia biology and force regulation and show for the first time that podosomes exert highly dynamic, piconewton vertical forces in epithelial cells.

cell biology↗

bioRxiv2018

A single test approach for accurate and sensitive detection and taxonomic characterization of Trypanosomes by comprehensive analysis of ITS1 amplicons

The World Health Organization has targeted stopping the transmission of Human African Trypanosomiasis by 2030. To achieve this, better tools are urgently required to identify and monitor Trypanosome infections in human, animals, and tsetse fly vectors. This study presents a single test approach for detection and identification of Trypanosomes and their comprehensive characterization at species and sub-group level. Our method uses newly designed ITS1 PCR primers (a widely used method for detection of African Trypanosomes, amplifying the ITS1 region of ribosomal RNA genes) coupled to Illumina sequencing of the amplicon. The protocol is based on the widely used Illuminas 16s bacterial metagenomic analysis procedure that makes use of multiplex PCR and dual indexing. We analyzed wild tsetse flies collected from Zambia and Zimbabwe. Our results show that the traditional method for Trypanosome species detection based on band size comparisons on a gel is unable to distinguish between T. vivax and T. godfreyi accurately. Additionally, this approach shows increased sensitivity of detection at species level. Through phylogenetic analysis, we identified Trypanosomes at species and sub-group level without the need for any additional tests. Our results show T. congolense Kilifi sub-group is more closely related to T. simiae than to other T. congolense sub-groups. This agrees with previous studies using satellite DNA and 18s RNA analysis. While current classification does not list any sub-groups for T. vivax and T. godfreyi, we observed distinct subgroups for these species. Interestingly, sequences matching T. congolense Tsavo (now classified as T. simiae Tsavo) clusters distinctly from the rest of the T. simiae Tsavo sequences suggesting that the Nannomonas group is more divergent than currently thought thus the need for a better classification criteria. This approach has the potential for refining classification of Trypanosomes and provide detailed molecular epidemiology information useful for surveillance and transmission control efforts.\n\nAuthor summaryDetection of Trypanosomes in the tsetse flies plays an important role in the control of African trypanosomiasis by providing information on circulating Trypanosome species in a given area. We have developed a method that combines multiplex PCR and next-generation sequencing for Trypanosome species detection. The method is based on the widely used bacterial metagenomic analysis protocol and uses a modular, two-step PCR process followed by sequencing of all amplicons in a single run, making sequencing of amplicons more efficient and cost-effective when dealing with large sample sizes. As part of this approach, we designed novel primers for amplifying the ITS1 region of the Trypanosome rRNA gene that is more sensitive than conventional primers. Identification of Trypanosome species is based on BLAST searches against the constantly updated NCBIs nt database, which facilitates the identification of Trypanosome subgroups. Our approach is more accurate than traditional gel-based analysis and shows how the latter is prone to misidentification. It is also sensitive and is able to discriminate between subgroups within Trypanosome species. Applied as an epidemiological tool, it has the potential to provide new, comprehensive and more accurate information on vector-pathogen-host interconnections which are key in the control and management of African trypanosomiasis.

epidemiology↗

bioRxiv2018

A major locus for ivermectin resistance in a parasitic nematode

BackgroundInfections with helminths cause an enormous disease burden in billions of animals and plants worldwide. Large scale use of anthelmintics has driven the evolution of resistance in a number of species that infect livestock and companion animals, and there are growing concerns regarding the reduced efficacy in some human-infective helminths. Understanding the mechanisms by which resistance evolves is the focus of increasing interest; robust genetic analysis of helminths is challenging, and although many candidate genes have been proposed, the genetic basis of resistance remains poorly resolved. ResultsHere, we present a genome-wide analysis of two genetic crosses between ivermectin resistant and sensitive isolates of the parasitic nematode Haemonchus contortus, an economically important gastrointestinal parasite of small ruminants and a model for anthelmintic research. Whole genome sequencing of parental populations, and key stages throughout the crosses, identified extensive genomic diversity that differentiates populations, but after backcrossing and selection, a single genomic quantitative trait locus (QTL) localised on chromosome V was revealed to be associated with ivermectin resistance. This QTL was common between the two geographically and genetically divergent resistant populations and did not include any leading candidate genes, suggestive of a previously uncharacterised mechanism and/or driver of resistance. Despite limited resolution due to low recombination in this region, population genetic analyses and novel evolutionary models supported strong selection at this Q.TL, driven by at least partial dominance of the resistant allele, and that large resistance-associated haplotype blocks were enriched in response to selection. ConclusionsWe have described the genetic architecture and mode of ivermectin selection, revealing a major genomic locus associated with ivermectin resistance, the most conclusive evidence to date in any parasitic nematode. This study highlights a novel genome-wide approach to the analysis of a genetic cross in non-model organisms with extreme genetic diversity, and the importance of a high quality reference genome in interpreting the signals of selection so identified.

genomics↗

bioRxiv2018

The dyslexia susceptibility KIAA0319 gene shows a highly specific expression pattern during zebrafish development supporting a role beyond neuronal migration.

Dyslexia is a common neurodevelopmental disorder that affects reading abilities and is predicted to be caused by a significant genetic component. Very few genetic susceptibility factors have been identified so far and amongst those, KIAA0319 is a key candidate. KIAA0319 is highly expressed during brain development but its function remains poorly understood. Initial RNA-interference studies in rats suggested a role in neuronal migration whereas subsequent work with double knock-out mouse models for both Kiaa0319 and its paralogue Kiaa0319-like reported effects in the auditory system but not in neuronal migration. To further understand the role of KIAA0319 during neurodevelopment, we carried out an expression study of the zebrafish orthologue at different embryonic stages. We report particularly high gene expression during the first few hours of development. At later stages, expression becomes localised in well-defined structures such as the eyes, the telencephalon and the notochord, supporting a role for kiaa0319 that is not restricted to brain development. Surprisingly, kiaa0319-like, which generally shows a similar expression pattern, was not expressed in the notochord suggesting a role specific to kiaa0319 in this structure. This study contributes to our understanding of KIAA0319 function during embryonic development which might involve additional roles in the visual system and in the notochord. Such a specific spatiotemporal expression pattern is likely to be under the controlled of tightly regulated sequences. Therefore, these data provide a framework to interpret the effects of the dyslexia-associated genetic variants that reside in KIAA0319 non-coding regulatory regions.

neuroscience↗