Biology subjects

Samal, A.

Publications and source records attributed to Samal, A..

49 records · Page 3Linked to original sources

bioRxiv2021

Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman-Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.

neuroscience↗

bioRxiv2021

Minimum complexity drives regulatory logic in Boolean models of living systems

The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network topology nor Boolean functions (or logical update rules) should be expected to be random. In this contribution, we focus on biologically meaningful types of Boolean functions, and perform a systematic study of their preponderance in gene regulatory networks. By applying the k[P] classification based on number of inputs k and bias P of functions, we find that most Boolean functions astonishingly have odd bias in a reference biological dataset of 2687 functions compiled from published models. Subsequently, we are able to explain this observation along with the enrichment of read-once functions (RoFs) and its subset, nested canalyzing functions (NCFs), in the reference dataset in terms of two complexity measures: Boolean complexity based on string lengths in formal logic which is yet unexplored in the biological context, and the average sensitivity. Minimizing the Boolean complexity naturally sifts out a subset of odd-biased Boolean functions which happen to be the RoFs. Finally, we provide an analytical proof that NCFs minimize not only the Boolean complexity, but also the average sensitivity in their k[P] set.

systems biology↗

bioRxiv2021

Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct a comprehensive endocrine-specific AOP (ED-AOP) network. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can be used to address the current knowledge gaps in the existing regulatory framework and aid in better risk assessment of environmental chemicals.

pharmacology and toxicology↗

bioRxiv2021

NeurotoxKb: compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals

Exposure to environmental neurotoxicants is a significant concern due to their potential to cause permanent or irreversible damage to the human nervous system. Here, we present the first dedicated knowledgebase, NeurotoxKb, on environmental neurotoxicants specific to mammals. Using a detailed workflow, we have compiled 475 potential non-biogenic neurotoxicants from 835 published studies with evidence of neurotoxicity specific to mammals. A unique feature of NeurotoxKb is the manual curation effort to compile and standardize the observed neurotoxic effects for the potential neurotoxicants from 835 published studies. For the 475 potential neurotoxicants, we have compiled diverse information such as chemical structures, environmental sources, chemical classification, physicochemical properties, molecular descriptors, predicted ADMET properties, and target human genes. To better understand the prospect of human exposure, we have explored the presence of potential neurotoxicants in external exposomes via two different analyses. By analyzing 55 chemical lists representing global regulations and guidelines, we reveal potential neurotoxicants both in regular use and produced in high volume. By analyzing human biospecimens, we reveal potential neurotoxicants detected in them. Lastly, a construction of the chemical similarity network and ensuing analysis revealed the diversity of the toxicological space of 475 potential neurotoxicants. NeurotoxKb is accessible online at: https://cb.imsc.res.in/neurotoxkb/.

pharmacology and toxicology↗

bioRxiv2020

MeFSAT: A curated natural product database specific to secondary metabolites of medicinal fungi

Fungi are a rich source of secondary metabolites which constitutes a valuable and diverse chemical space of natural products. Medicinal fungi have been used in traditional medicine to treat human ailments for centuries. To date, there is no devoted resource on secondary metabolites and therapeutic uses of medicinal fungi. Such a dedicated resource compiling dispersed information on medicinal fungi across published literature will facilitate ongoing efforts towards natural product based drug discovery. Here, we present the first comprehensive manually curated database on Medicinal Fungi Secondary metabolites And Therapeutics (MeFSAT) that compiles information on 184 medicinal fungi, 1830 secondary metabolites and 149 therapeutics uses. Importantly, MeFSAT contains a non-redundant in silico natural product library of 1830 secondary metabolites along with information on their chemical structures, computed physicochemical properties, drug-likeness properties, predicted ADMET properties, molecular descriptors and predicted human target proteins. By comparing the physicochemical properties of secondary metabolites in MeFSAT with other small molecules collections, we find that fungal secondary metabolites have high stereochemical complexity and shape complexity similar to other natural product libraries. Based on multiple scoring schemes, we have filtered a subset of 228 drug-like secondary metabolites in MeFSAT database. By constructing and analyzing chemical similarity networks, we show that the chemical space of secondary metabolites in MeFSAT is highly diverse. The compiled information in MeFSAT database is openly accessible at: https://cb.imsc.res.in/mefsat/.

bioinformatics↗

bioRxiv2020

Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, "deep learning" (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data - which we term "deep MVPA," or dMVPA - and introduce a new software toolbox (the "Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education" package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

neuroscience↗

bioRxiv2020

ExHuMId: A curated resource and analysis of Exposome of Human Milk across India

Human milk is a vital source of nourishment for infants, containing nutrients, immunoprotective components, and bioactive substances. However, several environmental contaminants find their way into human milk. Although lactation physiology has been well documented, the effect of human milk contaminants on maternal and infant health remains unclear. Human milk is the major route of contaminant exposure to infants; these contaminants and their effects can themselves be considered an exposome. While there are chemical regulations in India and scientific literature on environmental contaminants is available, yet there is a lack of systematic compilation, monitoring, and risk management of human milk contaminants. We have harnessed the potential of this large body of literature to develop the Exposome of Human Milk across India (ExHuMId) containing detailed information on 101 environmental contaminants detected in human milk samples, studied across 13 Indian states, compiled from 36 research articles. ExHuMId also compiles the detected concentrations of the contaminants, structural and physicochemical properties, and factors associated with the donor of the sample. Here, we also present findings from a three-pronged analysis of ExHuMId and two other resources on human milk contaminants, with a focus on the Indian scenario. Through a comparative analysis with global chemical regulations and guidelines, we identify human milk contaminants of high concern, such as potential carcinogens, endocrine disruptors and neurotoxins. We then study the physicochemical properties of the contaminants to gain insights on their propensity to transfer into human milk. Further, we employ a systems biology approach to shed light on potential effects of human milk contaminants on maternal and infant health, by identifying contaminant-gene interactions associated with lactation, cytokine signalling and production, and protein-mediated transport. ExHuMId is accessible online at: https://cb.imsc.res.in/exhumid/.

pharmacology and toxicology↗

bioRxiv2020

An exploratory analysis of the current chemical regulations and guidelines from the perspective of endocrine disrupting chemicals using public resources

The regulatory assessment of endocrine disrupting chemicals (EDCs) is complex due to the lack of a standardized definition of EDCs and validated testing criteria. In spite of these challenges, there is growing scientific interest in EDCs which has resulted in the rapid expansion of published literature on endocrine disruption upon chemical exposure. Here, we explore how academic research leading to curated knowledgebases can inform current chemical regulations on EDCs. To this end, we present an updated knowledgebase, DEDuCT 2.0, containing 792 potential EDCs with supporting evidence from 2218 research articles. Thereafter, we study the distribution of potential EDCs across several chemical lists that reflect guidelines for use or regulations. Further, to understand the scale of possible exposure to the potential EDCs present in chemical lists, we compare them with high production volume chemicals. Notably, we find many potential EDCs are in use across various product categories such as Food additives and Food contact materials and Cosmetics and household products. Several of these EDCs are also produced or manufactured in high volume across the world. Lastly, we illustrate using an example how diverse information in curated knowledgebases such as DEDuCT 2.0 can be helpful in the risk assessment of EDCs. In sum, we highlight the need to bridge the gap between academic and regulatory aspects of chemical safety, as a step towards the better management of environment and health hazards such as EDCs.

pharmacology and toxicology↗

bioRxiv2019

Broad substrate-specific phosphorylation events are associated with the initial stage of plant cell wall recognition in Neurospora crassa

Fungal plant cell wall degradation processes are governed by complex regulatory mechanisms, allowing the organisms to adapt their metabolic program with high specificity to the available substrates. While the uptake of representative plant cell wall mono- and disaccharides is known to induce specific transcriptional and translational responses, the processes related to early signal reception and transduction remain largely unkown. A fast and reversible way of signal transmission are post-translational protein modifications, such as phosphorylations, which could initiate rapid adaptations of the fungal metabolism to a new condition. To elucidate how changes in the initial substrate recognition phase of Neurospora crassa affect the global phosphorylation pattern, phospho-proteomics was performed after a short (2 minutes) induction period with several plant cell wall-related mono- and disaccharides. The MS/MS-based peptide analysis revealed large-scale substrate-specific protein phosphorylation and de-phosphorylations. Using the proteins identified by MS/MS, a protein-protein-interaction (PPI) network was constructed. The variance in phosphorylation of a large number of kinases, phosphatases and transcription factors indicate the participation of many known signaling pathways, including circadian responses, two-component regulatory systems, MAP kinases as well as the cAMP-dependent and heterotrimeric G-protein pathways. Adenylate cyclase, a key component of the cAMP pathway, was identified as a potential hub for carbon source-specific differential protein interactions. In addition, four phosphorylated F-Box proteins were identified, two of which, Fbx-19 and Fbx-22, were found to be involved in carbon catabolite repression responses. Overall, these results provide unprecedented and detailed insights into a so far less well known stage of the fungal response to environmental cues and allow to better elucidate the molecular mechanisms of sensory perception and signal transduction during plant cell wall degradation.

molecular biology↗

bioRxiv2019

A curated knowledgebase enabling a network perspective on endocrine disrupting chemicals and their biological systems-level perturbations

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents. Thereafter, this workflow was used to manually evaluate more than 16000 published research articles and identify 686 potential EDCs with published evidence in humans or rodents. Importantly, we have compiled the observed adverse effects or endocrine-specific perturbations along with the dosage information for the potential EDCs from their supporting published experiments. Subsequently, the potential EDCs were classified based on the type of supporting evidence, their environmental source and their chemical properties. Additional compiled information for potential EDCs include their chemical structure, physicochemical properties, predicted ADMET properties and target genes. In order to enable future research based on this compiled information on potential EDCs, we have built an online knowledgebase, Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT), accessible at: https://cb.imsc.res.in/deduct/. After building this comprehensive resource, we employed a network biology approach to study the chemical space of EDCs and its potential link to the biological space of target genes of EDCs. Specifically, we have constructed two networks of EDCs using our resource based on similarity of chemical structures or target genes. Ensuing analysis of these two networks revealed that EDCs can differ both in their chemical structure and set of target genes. Though our detailed results highlight potential challenges in developing predictive models for EDCs, the compiled information in our resource will undoubtedly enable future research in the field, especially, those focussed towards mechanistic understanding of the systems-level perturbations caused by EDCs.

bioinformatics↗

bioRxiv2017

Comparative systems analysis of the secretome of the opportunistic pathogen Aspergillus fumigatus and other Aspergillus species

Aspergillus fumigatus and multiple other Aspergillus species cause a wide range of lung infections, collectively termed aspergillosis. Aspergilli are ubiquitous in environment with healthy immune systems routinely eliminating inhaled conidia, however, Aspergilli can become an opportunistic pathogen in immune-compromised patients. The aspergillosis mortality rate and emergence of drug-resistance reveals an urgent need to identify novel targets. Secreted and cell membrane proteins play a critical role in fungal-host interactions and pathogenesis. Using a computational pipeline integrating data from high-throughput experiments and bioinformatic predictions, we have identified secreted and cell membrane proteins in ten Aspergillus species known to cause aspergillosis. Small secreted and effector-like proteins similar to agents of fungal-plant pathogenesis were also identified within each secretome. A comparison with humans revealed that at least 70% of Aspergillus secretomes has no sequence similarity with the human proteome. An analysis of antigenic qualities of Aspergillus proteins revealed that the secretome is significantly more antigenic than cell membrane proteins or the complete proteome. Finally, overlaying an expression dataset, four A. fumigatus proteins upregulated during infection and with available structures, were found to be structurally similar to known drug target proteins in other organisms, and were able to dock in silico with the respective drug.

bioinformatics↗

bioRxiv2017

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics

Phytochemical constituents of medicinal plants encompass a diverse space of chemical scaffolds which can be used for rational design of novel drugs. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable the application of systems biology and cheminformatic approaches towards natural product based drug discovery. In this direction, we here present, IMPPAT, a manually curated database of Indian Medicinal Plants, Phytochemistry, And Therapeutics. IMPPAT contains 1742 Indian medicinal plants, 9596 phytochemicals and 1124 therapeutic uses which span across 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico chemical library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical properties and drug-likeliness of the phytochemicals in IMPPAT which led to a filtered subset of 960 potential druggable phytochemicals. Moreover, a comparative analysis against FDA approved drugs suggests that majority of the druggable phytochemicals in IMPPAT are good candidates for novel prospective drugs as they have little or no structural similarity with existing drugs. The IMPPAT database is openly accessible at: https://www.imsc.res.in/~asamal/resources/imppat/home.

bioinformatics↗

bioRxiv2017

Network Reconstruction And Systems Analysis Of Plant Cell Wall Deconstruction By Neurospora crassa

Plant biomass degradation by fungal derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction. To expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa. To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighed to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network (PCWDN), leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of Neurospora crassa. Our network serves as a scaffold for integration of diverse experimental data, leading to elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi, and guiding efforts to rationally engineer industrially relevant hyper-production strains.

systems biology↗