Biology subjects

Mondal, S. S.

Publications and source records attributed to Mondal, S. S..

14 recordsLinked to original sources

bioRxiv2026

Superspreaders increase deleterious mutant burden and can accelerate the evolution of complex traits in pathogens

Degree heterogeneity in contact networks is known to accelerate the spread of infectious diseases through the presence of superspreaders, but its evolutionary consequences remain less understood. Here we study how network heterogeneity shapes the fate of competing pathogen strains in a stochastic susceptible-infected-susceptible framework. We show that heterogeneous networks act as strong suppressors of selection: both advantageous and disadvantageous mutants exhibit fixation probabilities close to neutral expectations, in stark contrast to well-mixed populations. We derive an analytical theory that captures this effect through a single suppression factor determined by network structure and infection dynamics, and validate it against simulations on synthetic and empirical contact networks. Mechanistically, suppression arises because most transmission events are effectively neutral, while selection acts only in rare configurations. As a consequence, heterogeneous networks substantially increase the persistence of deleterious mutants and elevate mutation-selection balance, but they can either accelerate or decelerate multi-step evolutionary processes such as fitness valley crossing. Our results reveal a fundamental trade-off induced by superspreaders: While they enhance epidemic spread, they weaken selective pressures and thereby promote evolutionary diversification.

evolutionary biology↗

bioRxiv2025

Cellular turnover can increase or decrease the mutant burden in expanding cell population

Expanding cell populations, including bacterial colonies and tumors, continuously accumulate mutations as they grow. Yet how mutant burden depends on the cellular death-to-birth ratio in spatially structured populations has not been addressed, despite clinical studies associating higher tumor turnover with both better and worse patient outcomes across cancers. This question is especially relevant for deleterious mutants, which can become advantageous if the environment changes, such as when treatment begins. Mutant accumulation has been studied extensively in well-mixed populations. Here, we derive a new closed-form approximation for the mean deleterious mutant burden at a specified population size when cell death is nonzero, serving as a baseline for comparisons to spatial structure. We show that spatial structure, a defining feature of solid tumors, can fundamentally alter how mutant burden depends on the death-to-birth ratio. Relationships that increase monotonically in well-mixed populations can become nonmonotonic or even decrease in spatially expanding populations, depending on population size and the fitness cost of mutants. These results provide a mechanistic framework for understanding why higher tumor turnover has been associated with either improved or worsened outcomes across patients and cancer types. An analysis of the probability that a colony regrows after an environmental change (such as treatment initiation) reveals a similar pattern: spatial structure can reverse or reshape this dependence. Overall, our findings establish spatial structure as a key determinant of how cellular death-to-birth ratios shape mutant accumulation, with implications for interpreting clinical data and predicting the burden of preexisting mutants during tumor evolution. Significance statementThe number of mutants present when a growing cell population reaches a given size shapes its capacity to adapt, since deleterious mutants can become advantageous once conditions change, such as when cancer treatment begins. Clinical studies report contradictory links between tumor cell turnover (death-to-birth ratio) and patient outcomes, with higher turnover linked to both better and worse prognosis. Building on established theory for well-mixed populations, we show that spatial population structure, a key feature of solid tumors, can make mutant burden increase, decrease, or vary non-monotonically with the death-to-birth ratio, depending on tumor size and mutant fitness cost, patterns absent in the well-mixed system. This resolves the contradiction and clarifies how tumor turnover shapes its potential to evolve and progress.

evolutionary biology↗

bioRxiv2023

The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice

The neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types 1-5, an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive. Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell 6-10 also known as extra telencephalic (ET) 11 neurons. Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive characterization of their local connectivity remains lacking. To address this knowledge gap, we leveraged a 1 mm3 electron microscopic (EM) dataset. We found that ET neurons primarily establish connections with inhibitory cells in their immediate vicinity. However, when they extend their axons to other cortical regions, they tend to connect more with excitatory cells. We also find that the inhibitory cells targeted by ET neurons are a specific group of cell types, and they preferentially inhibit ET cells. Finally, we observed that the most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. These findings challenge current views of the connectivity of ET neurons and suggest a circuit design that involves local competition among ET neurons and collaboration with other types of excitatory cells. Our results also highlight a specific circuit pattern where a subclass of excitatory cells forms a network with specific inhibitory cell types, offering a framework for exploring the connectivity of other types of excitatory cells.

neuroscience↗

bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

neuroscience↗

bioRxiv2023

Intercalation of the disulfide bond between the A2 and A4 loop of Cellobiohydrolase (Cel7A) of Aspergillus fumigatus enhances catalytic activity and thermostability

Disulfide bond is important for maintaining the structural conformation and stability of the protein. Introducing new disulfide bond is a promising strategy for rational protein design. In this report, disulfide bond engineering has been applied to improve the stability of an industrially important enzyme, Glycoside Hydrolase family GH 7 cellobiohydrolase (GH7 CBHs) or Cel7A of A fumigatus origin. Disulfide by Design 2.0 (DbD2), an online tool, was used for the detection of the mutation sites and created four mutations (D276C-G279C; DSB1, D322C-G327C; DSB2, T416C-I432C; DSB3, G460C-S465C; DSB4) both inside and outside of the peripheral loops but, not in the catalytic region. The disulfide bond formed between the A2 and A4 loop of DSB3 showed higher thermostability (70% activity at 700C), higher substrate affinity (Km= 0.081mM) and higher catalytic activity (Kcat =9.75 min-1; Kcat/Km = 120.37 mM min-1) compared to wild type AfCel7A (50% activity at 700C; Km= 0.128mM; Kcat = 4.833 min-1; Kcat/Km = 37.75 mM min -1). The other three mutants with high B factor showed loss of thermostability and catalytic activity. Molecular dynamic simulations revealed that the mutation T416C-I432C makes the tunnel wider (DSB3:13.6 [A]; Wt: 5.3 [A]) at the product exit site; giving flexibility in the entrance region and mobility of the substrate. It may facilitate substrate entry into the catalytic tunnel and releases the product faster than the wild type. Whereas in other mutants, the tunnel is not prominent (DSB4), the exit is lost (DSB1), and the ligand binding site is absent (DSB2). This is the first report of the gain of function of both thermostability and enzyme activity of cellobiohydrolase Cel7A by disulfide bond engineering in the loop. ImportanceCellulases are important for their role in the production of bioethanol, the cleanest renewable replacement of fossil fuels. Engineering of the cellulases is a chalange to increase their catalytic activity and thermostability for production of cheap ethanol. In this report we have introduced disulfide bond and successfully increased the both thermostabilty and catalytic activity of AfCel7A.

biochemistry↗

bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

neuroscience↗

bioRxiv2023

NEURD: A mesh decomposition framework forautomated proofreading and morphologicalanalysis of neuronal EM reconstructions

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

neuroscience↗

bioRxiv2023

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas -- including feedback connections -- suggesting the universality of the like-to-like connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting like-to-like connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

neuroscience↗

bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

neuroscience↗

bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

neuroscience↗

bioRxiv2021

Petascale neural circuit reconstruction: automated methods

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

neuroscience↗

bioRxiv2021

Targeting Integrated Stress Response by ISRIB combined with imatinib attenuates STAT5 signaling and eradicates therapy-resistant Chronic Myeloid Leukemia cells

Integrated Stress Response (ISR) facilitates cellular adaptation to variable environmental conditions by reprogramming cellular response. Activation of ISR was reported in neurological disorders and solid tumours, but its function in hematological malignancies remains largely unknown. Previously we showed that ISR is activated in chronic myeloid leukemia (CML) CD34+ cells, and its activity correlates with disease progression and imatinib resistance. Here we demonstrate that inhibition of ISR by small molecule ISRIB, but not by PERK inhibitor GSK2656157, restores sensitivity to imatinib and eliminates CM Blast Crisis (BC) D34+ resistant cells. We found that in Patient Derived Xenograft (PDX) mouse model bearing CD34+ imatinib/dasatinib-resistant CML blasts with PTPN11 gain-of-function mutation, combination of imatinib and ISRIB decreases leukemia engraftment. Furthermore, genes related to SGK3, RAS/RAF/MAPK, JAK2 and IFN{gamma} pathways were downregulated upon combined treatment. Remarkably, we confirmed that ISRIB and imatinib combination decreases STAT5 phosphorylation and inhibits expression of STAT5-target genes responsible for proliferation, viability and stress response. Thus, our data point to a substantial effect of imatinib and ISRIB combination, that results in transcriptomic deregulation and eradication of imatinib-resistant cells. Our findings suggest such drug combination might improve therapeutic outcome of TKI-resistant leukemia patients exhibiting constitutive STAT5 activation.

cancer biology↗

bioRxiv2020

The hunt for ancient prions: Archaeal prion-like domains form amyloids and substitute for yeast prion domains

Prions are proteins capable of acquiring an alternate conformation that can then induce additional copies to adopt this same alternate conformation. Although initially discovered in relation to mammalian disease, subsequent studies have revealed the presence of prions in Bacteria and Viruses, suggesting an ancient evolutionary origin. Here we explore whether prions exist in Archaea - the last domain of life left unexplored with regard to prions. After searching for potential prion-forming protein sequences computationally, we tested candidates in vitro and in organisms from the two other domains of life: Escherichia coli and Saccharomyces cerevisiae. Out of the 16 candidate prion-forming domains tested, 8 bound to amyloid-specific dye, and six acted as protein-based elements of information transfer, driving non-Mendelian patterns of inheritance. We additionally identified short peptides from archaeal prion candidates that can form amyloid fibrils independently. Candidates that tested positively in our assays had significantly higher tyrosine and phenylalanine content than candidates that tested negatively, suggesting that the presence of these amino acids may help distinguish functional prion domains from nonfunctional ones. Our data establish the presence of amyloid-forming prion-like domains in Archaea. Their discovery in all three domains of life further suggests the possibility that they were present at the time of the last universal common ancestor (LUCA). Significance StatementThis work establishes that amyloid-forming, prion-like domains exist in Archaea and are capable of vertically transmitting their prion phenotype - allowing them to function as protein-based elements of inheritance. These observations, coupled with prior discoveries in Eukarya and Bacteria, suggest that prion-based self-assembly was likely present in lifes last universal common ancestor (LUCA), and therefore may be one of the most ancient epigenetic mechanisms.

evolutionary biology↗