Biology subjects

Ayub, M.

Publications and source records attributed to Ayub, M..

12 recordsLinked to original sources

bioRxiv2026

Foxg1 promotes neuron allocation to engrams and facilitates fear memorization

In addition to orchestrate telencephalic development, the transcription factor Foxg1 is involved in mutual positive feedback with neuronal activity. Based on that, we hypothesized an involvement of it in learning and engram dynamics. We observed that its sparse and mild neuronal upregulation improved learning abilities as evaluated by a CFC assay. This effect was specifically pronounced upon early memory retrieval and disappeared when Foxg1-GOF neurons were silenced by a Gi DREADD effector. A prevailing positive relationship was detectable between Foxg1 expression level and the probability of a neuron getting recruited into training and recall engrams, in structures involved in both short- and long-term memory formation. Moreover, Foxg1 upregulation elicited a generalized shrinkage of engrams and increased the fraction of late recall engram cells already active at the time of training. Together, these findings establish Foxg1 as a key effector linking neuronal excitability to engram allocation and memory recall.

neuroscience↗

bioRxiv2025

Functional organization of multisensory integration network in children and youth with neurodevelopmental disorders predicts clinical sensory issues

Differences in sensory processing in neurodevelopmental conditions, including Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD), cascade into downstream clinical symptomatology. This includes differences in combining sensory information from multiple modalities into a unified percept, known as multisensory integration. Little is known about the functional organization of multisensory network (MSN) in these groups, its relation to clinical sensory issues, or its interaction with other higher-order cortical networks. We examined resting-state fMRI data from 417 participants in the Province of Ontario Neurodevelopmental Network (ASD=174, ADHD=130, Typical Development=113; Mean age=11.96{+/-}4.10). Timeseries data were extracted from the MSN and seven additional resting-state cortical networks (RSNs). Undirected and directed functional connectivity (FC) metrics were computed within the MSN and between the MSN and other RSNs. FC was compared across diagnoses and related to clinical sensory characteristics. The thalamus emerged as a hub region in both undirected and directed FC within the MSN and between the MSN-RSNs. Some diagnosis-related differences were observed, with increased MSN-RSN FC particularly in ADHD; however, associations with sensory characteristics were stronger in both undirected and directed FC within the MSN and between the MSN-RSNs, regardless of diagnosis. Converging evidence was seen in data-driven clusters based on FC metrics, which did not align with diagnosis, but instead mapped on to the overall level of sensory issues reported. That the data-driven clusters sorted not by diagnosis but by sensory characteristics suggests that these sensory characteristics and their underlying neurobiology are transdiagnostic in nature as opposed to specific to ASD or ADHD.

neuroscience↗

bioRxiv2025

Varicose-projection astrocytes: a reactive phenotype associated with neuropathology

Glial cells are fundamental for the pathophysiology of all neurological disorders. Astrocytes, the primary home-ostatic cells of the central nervous system (CNS), exhibit species-specific characteristics, with human astrocytes specifically displaying unique structural and functional features. It is thus essential to investigate human-specific astrocytic responses to neuropathology using human-relevant models. Varicose projection (VP) astrocytes, traditionally considered specific to humans and apes, were suggested to reflect pathological burden, albeit direct evidence linking them to neurological diseases has been lacking. Here, we demonstrate for the first time that VP astrocytes are present in mice and tigers (Panthera tigris) and we provide evidence from four distinct human-based models that VP astrocytes are not a distinct physiological astrocyte subtype but rather a novel class of reactive astrocytes associated with neuropathology. Using human induced pluripotent stem cell (hiPSC)-derived astrocytes, mixed neural cultures, and cortical organoids, we showed that VP astrocytes are induced by pro-inflammatory cytokines interleukin-1{beta} (IL-1{beta}) and tumor necrosis factor- (TNF-) or LPS. Notably, cytokine withdrawal reverses the VP phenotype of astrocytes, indicating that it is a transient, inflammation-dependent state. We characterized the distinctive components of varicosities, including markers for extracellular vesicles, mitochondria, Golgi and endoplasmic reticulum components, suggesting roles in cellular stress responses and metabolic dysregulation. We further validated the pathological relevance of VP astrocytes by documenting their significant enrichment in postmortem brain samples from patients with several neurodegenerative diseases including as Alzheimers disease, Parkinsons disease, and multiple sclerosis, as well as in surgical resections from patients with epilepsy due to hippocampal sclerosis or brain tumors, including previously unreported subcortical regions such as basal ganglia. Additionally, we identified a higher number of VP astrocytes also in mouse astrocytes upon treatment with pro-inflammatory cytokines, suggesting that formation of VP astrocytes is an evolutionarily conserved astrocytic response to neuroinflammation. Our findings point to VP astrocytes as a novel reactive astrocyte subtype closely linked to neuropathology, highlighting their potential as biomarkers and therapeutic targets in neurological diseases. This study lays the groundwork for future investigations into the mechanisms driving VP astrocyte formation and their broader implications in neuropathology.

neuroscience↗

bioRxiv2025

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders

The heterogeneity of autism and related neurodevelopmental conditions has impeded accurate prognoses and treatment discovery. Using translational neuroimaging across 135 mouse models (3,515 mice) and two human MRI datasets (n = 1,234 and n = 1,015), we derived participant subgroups from shared neuroanatomical features. These subgroups did not distinguish autism, ADHD, or OCD diagnoses and only modestly differentiated cognitive and behavioural phenotypes. Instead, they mapped onto four molecular pathways: (1) synaptic function; (2) MAPK and Wnt signalling; (3) chromatin modification and cellular stress responses; and (4) broader chromatin, immune, and second-messenger signalling pathways. This framework bridges preclinical models and idiopathic human neurodevelopmental conditions, linking patients to biologically relevant molecular mechanisms.

neuroscience↗

bioRxiv2025

Decoding the neural basis of sensory phenotypes in Autism

BackgroundDifferences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Recent research identified five sensory processing phenotypes with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity. MethodsWe analyzed data from 146 autistic participants in the Province of Ontario Neurodevelopmental Network. We classified participants into sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes. ResultsWe replicated that our sample of autistic participants was best categorized into five sensory phenotypes. The machine learning models distinguished 7/10 phenotype pairs using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas were highly predictive of sensory phenotype. ConclusionsThis study shows that distinct sensory phenotypes in autism correspond with unique patterns of functional connectivity. Cortical, subcortical, and network-level connectivity all play a role in shaping distinct sensory processing styles in autism. These findings lay the groundwork for understanding these phenotypes and highlight opportunities to develop interventions in cases of maladaptive sensory processing.

neuroscience↗

bioRxiv2024

Hydralazine inhibits cysteamine dioxygenase to treat preeclampsia and senesce glioblastoma

The vasodilator hydralazine (HYZ) has been used clinically for [~] 70 years and remains on the World Health Organizations List of Essential Medicines as a therapy for preeclampsia. Despite its longstanding use and the concomitant progress toward a general understanding of vasodilation, the target and mechanism of HYZ have remained unknown. We show that HYZ selectively targets 2-aminoethanethiol dioxygenase (ADO) by chelating its metal cofactor and alkylating one of its ligands. This covalent inactivation slows entry of proteins into the Cys/N-degron pathway that ADO initiates. HYZs capacity to stabilize regulators of G-protein signaling (RGS4/5) normally marked for degradation by ADO explains its effect on blood vessel tension and comports with prior associations of insufficient RGS levels with human preeclampsia and analogous symptoms in mice. The established importance of ADO in glioblastoma led us to test HYZ in these cell types. Indeed, a single treatment induced senescence, suggesting a potential new HYZ-based therapy for this deadly brain cancer.

biochemistry↗

bioRxiv2024

Using deep learning to predict internalizing problems from brain structure

Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning models that used measures of brain structure (thickness, surface area, and volume) to (a) predict clinically significant internalizing problems cross-sectionally (N=14,523); and (b) predict subsequent worsening trajectories (using the reliable change index) of internalizing problems (N=10,540) longitudinally. A stratified cross-validation scheme was used to tune, train, and test the models, which were evaluated using the area under the receiving operating characteristic curve (AUC). The cross-sectional model performed well across the sample, reaching an AUC of 0.80[95% CI: 0.71,0.88]. For the longitudinal model, while performance was sub-optimal for predicting worsening trajectories in a sample of the general population (AUC=0.66[0.65,0.67]), good performance was achieved in a small, external test set of primarily ND conditions (AUC=0.80[0.78,0.81]), as well as across all ND conditions (AUC=0.73[0.70,0.76]). Deep learning with features of brain structure is a promising avenue for biomarkers of internalizing problems, particularly for individuals who have a higher likelihood of experiencing difficulties.

neuroscience↗

bioRxiv2024

Melanopsin ganglion cells in the mouse retina independently evoke pupillary light reflex

PurposeThe pupillary light reflex (PLR) is crucial for protecting the retina from bright light. The intrinsic photosensitive ganglion cells (ipRGCs) in the retina mediate the PLR, which directly sense light and receive inputs from rod/cone photoreceptors. Previous work used genetic knockout mice to reveal that rod/cone photoreceptors drive transient constriction, and ipRGCs drive the sustained component. We acutely ablated photoreceptors by a chemical injection to examine the role of rod and cone photoreceptors in PLR. MethodsPLR and the multiple electrode array (MEA) recording were conducted with C57BL6/J (wildtype: WT) and Cnga3-/-; Gnat1-/- (rod/cone dysfunctional) mice. n-Nitroso-n-methylurea (MNU) was applied to C57 mice by intraperitoneal injection, and PLR was conducted after 5-7 days of injection. Three different light levels (mesopic, low photopic, and high photopic) were tested. Immunohistochemistry was conducted using the anti-Gnat1 and anti-melanopsin antibodies with DAPI. ResultsPLR was induced by all light levels we tested, and the level of constriction increased as the light level increased. After the MNU injection, PLR was not induced at mesopic light stimulus, but was fully induced by high light. The level of PLR was identical between WT and MNU mice, suggesting that ipRGCs fully contributed to the PLR at this light level. Immunohistochemistry revealed that photoreceptors were ablated by the MNU injection, but ipRGCs were preserved. The MEA recording revealed that a population of ipRGCs generated fast and robust spikes in MNU-injected retinal tissues in ex vivo. ConclusionsContrary to previous observations, our results demonstrate that ipRGCs are the major contributor to the PLR induced by high light.

neuroscience↗

bioRxiv2024

Dataset factors influencing age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets, and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N=1,218) and function (resting-state functional connectivity strength; N=1,254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental network (POND) and the Healthy Brain Network (HBN). We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

neuroscience↗

bioRxiv2021

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.

neuroscience↗

bioRxiv2020

Chasing the origin of SARS-CoV-2 in Canada's COVID-19 cases: A genomics study

The emergence and global spread of SARS-CoV-2 has had profound social and economic consequences and has shed light on the importance of continued and additional investment in global health and infectious disease surveillance. Identifying changes in viral genomes provides key insights into viral diversity, how viruses spread within populations, and viral strategies for evasion of host immune systems. Here we report twenty-five SARS-CoV-2 genome sequences collected from some of the first COVID-19 cases in eastern Ontario, Canada (March 18-30, 2020). The reported genomes belong to the S-clade (n=2) and G-clade (n=23) of SARS-CoV-2 and contain 45 polymorphic sites including one shared missense and three unique synonymous variants in the gene encoding the spike protein. A phylogenetic analysis enabled the tracing of viral origin and potential transmission into and within Canada. There may be as many as sixteen unique infection events represented in these samples, including at least three that were likely introduced from Europe and seven from the USA. In addition, four separate genomes are each shared by multiple patients, suggesting a common origin or community spread even during this early stage of infection. These results demonstrate how molecular epidemiology and evolutionary phylogenetics can help local health units track origins and vectors of spread for emerging diseases like SARS-CoV-2. Earlier detection and screening in this way could improve the effectiveness of regional public health interventions to prevent future pandemics.

microbiology↗

bioRxiv2016

Mapping Autosomal Recessive Intellectual Disability: Combined Microarray and Exome Sequencing Identifies 26 Novel Candidate Genes in 192 Consanguineous Families

Approximately 1% of the global population is affected by intellectual disability (ID), and the majority receive no molecular diagnosis. Previous studies have indicated high levels of genetic heterogeneity, with estimates of more than 2500 autosomal ID genes, the majority of which are autosomal recessive (AR). Here, we combined microarray genotyping, homozygosity-by-descent (HBD) mapping, copy number variation (CNV) analysis, and whole exome sequencing (WES) to identify disease genes/mutations in 192 multiplex Pakistani and Iranian consanguineous families with non-syndromic ID. We identified definite or candidate mutations (or CNVs) in 51% of families in 72 different genes, including 26 not previously reported for ARID. The new ARID genes include nine with loss-of-function mutations (ABI2, MAPK8, MPDZ, PIDD1, SLAIN1, TBC1D23, TRAPPC6B, UBA7, and USP44), and missense mutations include the first reports of variants in BDNF or TET1 associated with ID. The genes identified also showed overlap with de novo gene sets for other neuropsychiatric disorders. Transcriptional studies showed prominent expression in the prenatal brain. The high yield of AR mutations for ID indicated that this approach has excellent clinical potential and should inform clinical diagnostics, including clinical whole exome and genome sequencing, for populations in which consanguinity is common. As with other AR disorders, the relevance will also apply to outbred populations.

genetics↗