Biology subjects

Imam, N.

Publications and source records attributed to Imam, N..

10 recordsLinked to original sources

bioRxiv2026

Inverse covariation between the neocortex and the limbic system is concentrated at deep evolutionary divergences

The macroscale architecture of mammalian brains consists of a broadly conserved set of structures, but the relative sizes of these structures vary substantially across species. Much of this variation follows conserved scaling relationships, but particular systems can expand or contract relative to the rest of the brain, providing targets for adaptive selection. One prominent example is a volumetric tradeoff between the neocortex and the limbic system, in which expansion of one is associated with reduction of the other, a feature apparent when brain structure volumes are examined across living species. However, whether this relationship has operated throughout mammalian evolution or arose during particular periods of diversification is unknown. Using phylogenetic comparative methods, we here show that the tradeoff between the neocortex and the limbic system is concentrated at deep evolutionary divergences separating major mammalian groups. After accounting for shared ancestry and brain size, the tradeoff strengthens progressively among deeper branches of the mammalian phylogeny, extending broadly across limbic structures, with the strongest signal involving olfactory components. Recent divergences show no consistent relationship. Our analyses indicate that the neocortex-limbic tradeoff emerged during the early diversification of mammals, highlighting how present-day variation in brain organization retains signatures of ancient evolutionary transitions.

neuroscience↗

bioRxiv2026

Geometric constraints in the development of primate extrastriate visual cortex

Sensory systems are organized into topographic maps that shape information flow and computation across cortical circuits. Although the mechanisms establishing primary sensory maps are well characterized, how higher-order maps emerge across neocortex is unclear. Because the probability and strength of cortical connections fall off steeply with distance along a folded surface, the geometry of the cortex may be a major factor shaping the organization of higher-order maps. To test this in a well-characterized sensory system, we develop a growth model embedded in the folded surface geometry of the macaque visual cortex, using fMRI-defined V1 retinotopy as the sole functional anchor. Cortical organization emerges through algorithmic growth from primary visual cortex, governed by distance-dependent activity correlations and competition among developing projections. Without imposing areal boundaries, map orientations, or predefined topographic layouts, this process generates multiple retinotopic maps with systematic mirror reversals and smooth gradients that reflect key structural features of fMRI-derived extrastriate maps. Parameters estimated on a population template generalize across individual macaques, while individual cortical geometry accounts for fine-scale map variation around a common retinotopic scaffold. These results suggest that conserved growth rules acting on folded cortical surfaces produce stereotyped higher-order retinotopic organization under minimal explicit specification.

neuroscience↗

bioRxiv2025

Neural rhythms as priors of speech computations

The transformation of speech into discrete linguistic representations forms the basis of speech recognition. Natural speech encodes cues at distinct timescales: phonetic features have modulation frequencies of 30-50 Hz, syllables and words around 4-7 Hz, and phrases 1-2 Hz. Strikingly, these frequencies mirror frequencies of endogenous network rhythms of the brain and synaptic time constants of the underlying neural circuits. Here, we suggest that endogenous brain rhythms serve as priors for speech recognition, encoding knowledge of speech structure in the dynamics of network computations. In a network of coupled oscillators, we find that speech is readily identified when characteristic frequencies of the oscillators match frequencies of circuit rhythms in the brain. When signal and circuit rhythms are mismatched, speech identification is impaired. Compared to a baseline recurrent neural network without intrinsic oscillations, the coupled oscillatory network has significantly higher performance in speech recognition across languages, but not in the recognition of signals that lack speech-like structure, such as urban sounds. Our results suggest a central computational role of brain rhythms in speech processing.

neuroscience↗

bioRxiv2024

Dual Computational Systems in the Development and Evolution of Mammalian Brains

Analyses of brain sizes across mammalian taxonomic groups reveal a consistent pattern of covariation between major brain components, including a robust inverse covariation between the limbic system and the neocortex. To find the functional basis of this inverse relationship, we mapped the multidimensional representations of task-optimized machine learning systems onto two-dimensional surfaces resembling the forebrain cortices. We found that visual, somatosensory and auditory systems develop ordered spatiotopic maps where units draw information from localized regions of the sensory input. Olfactory and relational memory systems, in contrast, develop fractured maps with distributed patterns of information convergence. Evolutionary optimization of multimodal systems for varying task objectives results in inverse covariation between spatiotopic and disordered system components that compete for representational space. These results suggest that the observed pattern of covariation between brain components reflect an essential computational duality in brain evolution.

neuroscience↗

bioRxiv2022

Deciphering the conformational dynamics of gephyrin-mediated collybistin activation

Efficient neuronal signaling depends on the proper assembly of the postsynaptic neurotransmitter machinery and at inhibitory GABAergic synapses is controlled by the scaffolding protein gephyrin and collybistin, a Dbl-family guanine nucleotide exchange factor and neuronal adaptor protein. Collybistin usually contains an N-terminal SH3 domain and exists in closed/inactive or open/active states. Here, we elucidate the molecular basis of the gephyrin-collybistin interaction with newly designed collybistin FRET sensors. Using fluorescence lifetime-based FRET measurements, we deduce the affinity of the gephyrin-collybistin complex, thereby confirming that the C-terminal dimer-forming E domain binds collybistin, an interaction, which does not require E domain dimerization. Simulations based on fluorescence lifetime and sensor distance distributions reveal a dynamic behavior of the SH3 domain already in the closed state of collybistin. Finally, our data provide strong evidence for a collybistin-gephyrin communication network, where, unexpectedly, switching of collybistin from closed/inactive to open/active states is efficiently triggered by gephyrin.

biophysics↗

bioRxiv2021

Soil Microbial Composition and Structure Allow Assessment of Biological Product Effectiveness and Crop Yield Prediction

Understanding the effectiveness and potential mechanism of action of agricultural biological products under different soil profiles and crops will allow more precise product recommendations based on local conditions and will ultimately result in increased crop yield. This study aimed to use bulk and rhizosphere soils microbial composition and structure to evaluate the effect of a Bacillus amyloliquefaciens strain QST713 inoculant on potatoes, and to explore its relationship with crop yield. We implemented NGS and bioinformatics approaches to assess the bacterial and fungal biodiversity in 185 soil samples, distributed over four different time points -from planting to harvest -from three different geographical regions in the United States. In addition to variety, phenological stage of the potato plant and geography being important factors defining the microbiome composition and structure, the microbial inoculant applied as a treatment also had a significant effect. However, treatment preserved the native communities without causing a detectable long-lasting effect on the alpha- and beta-diversity patterns after harvest. Specific taxonomic groups, and most interestingly the structure of the fungal and bacterial communities (measured using co-occurrence and co-exclusion networks), changed after inoculation. Additionally, using information about the application of the microbial inoculant and considering microbiome composition and structure data we were able to train a Random Forest model to estimate if a bulk or rhizosphere soil sample came from a low or high yield block with relatively high accuracy, concluding that the structure of fungal communities is a better estimator of potato yield than the structure of bacterial communities. IMPORTANCEThe manuscripts results reinforce the notion that each crop variety on each location recruits a unique microbial community and that these communities are modulated by the vegetative growth stage of the plant. Moreover, inoculation of a Bacillus amyloliquefaciens strain QST713-based product on potatoes also changed specific taxonomic groups and, most interestingly, the structure of local fungal and bacterial networks in bulk and rhizosphere soil. The data obtained, coming from in-field assays performed in three different geographical locations, allowed training a predictive model to estimate the yield of a certain block, identifying microbiome variables -especially those related to microbial community structure- with a higher predictive power than the variety and geography of the block. The methods described here can be replicated to fit new models predicting yield in any other crop, and to evaluate the effect of any Ag-input product in the composition and structure of the soil microbiome.

microbiology↗

bioRxiv2020

Microbial map of the world's vineyards:Applying the concept of microbial terroir on a global scale

The specific microbial biodiversity linked to a particular vineyard location is reported to be a crucial aspect, in conjunction with edaphic, climatic and human factors, in the concept of wine terroir. These biogeographical patterns are known as microbial terroirs. This study applied an HTS amplicon library approach in order to conduct a global survey of vineyards soil microbial communities. In all, soil samples from 200 vineyards on four continents were analysed in an attempt to establish the basis for the development of a vineyard soil microbiome map to represent microbial wine terroirs on a global scale. This study established links between vineyard locations and microbial biodiversity on different scales: between continents and countries, and between different wine regions within the same country. Geography had a strong effect on the composition of microbial communities on a global scale, which was also maintained on a country scale. Furthermore, a predictive model was developed, based on random forest analyses, to discriminate between microbial patterns in order to identify the geographical source of the samples with reasonable precision. Finally this study is the first to describe the microbial community of new and northern wine-producing regions, such as Denmark, that could be of great interest for viticulture adaptation in a context of climate change.

microbiology↗

bioRxiv2020

Self-organization of cortical areas in the development and evolution of neocortex: a network growth model

While the mechanisms generating the topographic organization of primary sensory areas in the neocortex are well-studied, what generates secondary cortical areas is virtually unknown. Using physical parameters representing primary and secondary visual areas as they vary from monkey to mouse, we derived a growth model to explore if characteristic features of secondary areas could be produced from correlated activity patterns arising from V1 alone. We found that V1 seeded variable numbers of secondary areas based on activity-driven wiring and wiring density limits within the cortical surface. These secondary areas exhibited the typical mirror-reversal of map topography on cortical area boundaries and progressive reduction of the area and spatial resolution of each new map on the caudorostral axis. Activity-based map formation may be the basic mechanism that establishes the matrix of topographically-organized cortical areas available for later computational specialization.

neuroscience↗

bioRxiv2020

Design of an Epitope-Based Peptide Vaccine against the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2): A Vaccine Informatics Approach

The recurrent and recent global outbreak of SARS-CoV-2 has turned into a global concern which has infected more than 19-million people all over the globe, and this number is increasing in hours. Unfortunate no vaccine or specific treatment is available, which make it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides (including T and B cells) in the surface glycoprotein of SARS-CoV-2 which showed non-toxic nature, non-allergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that CD4+ T-cell peptides showed higher cumulative population coverage over to CD8+ peptides in the 16 different geographical regions of the world. We identified twelve peptides (LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are 80% - 90% identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that identified peptides are tightly bound in the groove of HLA molecules which can induce the T-cell response. Overall this study allows us to determine potent peptide antigen targets in surface glycoprotein on intuitive grounds which open up a new horizon in COVID-19 research. However, this study needs experimental validation by in vitro and in vivo.

bioinformatics↗

bioRxiv2018

Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

BackgroundTuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker.\n\nObjectivesWe want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body.\n\nResultsWe identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathways importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression.\n\nConclusionsThis study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.

systems biology↗