Biology subjects

Samal, A.

Publications and source records attributed to Samal, A..

At least 19 recordsLinked to original sources

bioRxiv2026

NANHA - Neurostimulation in Atypical Neurodevelopment: a Harmonized Atlas

Non-invasive brain stimulation (NIBS) has become an important tool for investigating brain function and exploring therapeutic intervention in neurodevelopmental disorders (NDDs). Nevertheless, relevant studies are scattered across different NDDs, stimulation modalities, target regions, and experimental designs, which makes systematic exploration and comparative analysis challenging. Therefore, we present NANHA, a manually curated, harmonized and freely accessible resource of published NIBS studies in NDDs. The database was constructed using systematic PubMed searches covering 22 NDDs and five NIBS modalities. NANHA captures participant characteristics, study design, stimulation purpose and parameters, targeted brain regions, behavioral, cognitive, and neurophysiological outcomes, safety information, and follow-up assessments, whenever available. The NANHA web platform (https://cb.imsc.res.in/nanha/) provides searchable and downloadable records, advanced filtering options, and interactive visualizations to support data exploration. Overall, NANHA is a standardized resource for evidence synthesis, comparative analyses, identification of research gaps, and the development of disease-specific stimulation protocols for NDDs.

neuroscience↗

bioRxiv2026

TickMapKB: A FAIR Spatial Knowledgebase of Tick Species and Associated Pathogens in India

Ticks transmit pathogens across wildlife, domestic animals, and humans, and are therefore considered important vectors under the One Health framework. In India, the burden of tick-borne diseases, such as Kyasanur Forest Disease and Crimean-Congo Hemorrhagic Fever, remains poorly quantified due to fragmented surveillance and limited spatial data. Here, we present TickMapKB, a curated spatial knowledgebase documenting 72 tick species across more than 600 georeferenced locations in India. The majority of the species belonged to the genera Haemaphysalis, Rhipicephalus, and Hyalomma. Additionally, the resource integrates host associations, pathogen and disease information, morphological identification keys, acaricide resistance profiles, and protein annotations into a single, interactive platform accessible at https://cb.imsc.res.in/tickmapkb/. Specifically, the resource captures 53 pathogens, over 3000 protein annotations, and morphological keys from 26 published resources. TickMapKB thus provides researchers, clinicians, and other stakeholders with integrated spatial, biological, and resistance information to support surveillance planning, resistance management, and tick-borne disease risk assessment.

ecology↗

bioRxiv2026

An integrated resource for systems-level analysis of aging hallmarks and associated genes

Aging is a complex biological process involving progressive cellular dysfunction, tissue decline, and increased susceptibility to multiple chronic diseases. A systemic view of aging through its established hallmarks provides a structured framework to understand this complexity and drive therapeutic discovery. To this end, we present AgingHallmarksDB, an interactive web platform that enables systems-level analysis of hallmark-associated gene sets. Aging-related genes were first curated from seven established resources, and those present in at least two of these resources were considered as consensus aging-related genes. Using functional annotations derived from GO, KEGG, and Reactome, a total of 3111 genes were mapped to the 11 aging hallmarks, of which 2593 were supported by additional experimental or manually curated evidence, with 1089 of these forming the consensus set. Further, AgingHallmarksDB supplements hallmarks and gene annotations with tissue or cell-type class specificity, exosomal profiles, and regulatory interactions, enabling users to perform hallmark enrichment, protein-protein and regulatory interaction-based analysis. To elucidate the interconnectedness of hallmarks, network separation and proximity analyses of hallmark-associated modules were performed, revealing functional overlap between hallmarks on the human interactome. Furthermore, the utility of AgingHallmarksDB was demonstrated through network topological and regulatory interaction analyses of common hallmark genes, hallmark enrichment and network proximity analyses of eight chronic age-related diseases and seven developmental and congenital diseases, and gene set enrichment analysis of a PM2.5-associated skin transcriptome. Together, these analyses highlight the utility of AgingHallmarksDB for aging hallmark-centred systems biology research. The resource is accessible at https://cb.imsc.res.in/aginghallmarksdb/.

bioinformatics↗

bioRxiv2026

Benchmarking Artificial Intelligence Models for Predicting Nuclear Receptor Activity from Tox21 Assays

Tox21 assays compile extensive chemical bioactivity data across diverse biological targets, making them widely utilized resources for in silico model development. Nuclear receptor-specific assays within this dataset are particularly valuable for screening potential endocrine disrupting chemicals. This study presents a comprehensive benchmarking of diverse machine learning (ML), deep learning (DL), and transformer-based architectures with varied chemical feature representations across nuclear receptor assays. First, 43 datasets associated with 18 nuclear receptors within Tox21 assays were systematically curated from ToxCast invitrodb v4.3. Upon testing across these datasets, model performance was found to be dependent on the degree of class imbalance. Tree-based ML models such as random forest (RF) and extreme gradient boosting (XGBoost) trained on descriptors, or combination of descriptors and fingerprints, consistently outperformed in datasets with higher proportions of active chemicals (>10%), while DL models showed greater robustness for those with moderate proportions (5-10%). Further analysis revealed that approximately 40% of misclassified active chemicals occupied structurally isolated regions of the chemical space, suggesting absence of close structural analogues in the training set potentially contributed to their misclassification. External validation using in vitro and in vivo androgen and estrogen receptor bioactivity data showed generally good concordance. Finally, a systematic literature review revealed that the models in this study span wider range of architectures, feature representations, and assay endpoints, and are broadly comparable to or better than existing work. Overall, insights from this study can inform the development of more reliable in silico tools supporting new approach methodologies for nuclear receptor bioactivity predictions.

pharmacology and toxicology↗

bioRxiv2026

A comprehensive resource on chemicals used in aquaculture and their ecotoxicity

Sustainable aquaculture requires comprehensive chemical oversight, as compounds used in aquaculture can persist in ecosystems, bioaccumulate through food chains, and affect aquatic life and human health. This study presents ReCAnt (Resource on Chemicals used in Aquaculture and their Ecotoxicity), which compiles information on 690 aquaculture chemicals, with data on toxic effects and therapeutic potential curated from published literature. It was observed that only a fraction of the 690 chemicals are currently regulated, revealing gaps in existing regulations. Integration of data from the Comparative Toxicogenomics Database revealed associations with genes, phenotypes, and diseases, while ECOTOX data provided toxicity and bioconcentration information. Predicted biotransformation pathways and partition coefficients indicated microbial degradation potential and fate across environmental media. Further, food web network analysis identified species vulnerable to trophic transfer and common entry points for chemicals into aquatic ecosystems. This resource can aid in developing evidence-based regulatory frameworks and promoting sustainable chemical management.

pharmacology and toxicology↗

bioRxiv2026

DEDuCT 3.0: An enhanced and expanded FAIR-compliant resource and toxicology knowledge graph for endocrine disrupting chemicals

Endocrine disrupting chemicals (EDCs) are of particular regulatory and research interest due to the increasing incidence of endocrine-related disorders, such as declining fertility rates and reproductive health problems. The Database of Endocrine Disrupting Chemicals and their Toxicity Profiles (DEDuCT) has gained importance in both academic and regulatory settings by systematically curating data from published literature to characterize these chemicals. Given the growing body of EDC literature, this study aimed to consolidate the latest research and update this critical database. First, more than 14000 research articles were screened through an extensive four-stage manual process, and integrated with the earlier version to create the updated DEDuCTv3.0, comprising 1043 unique EDCs and 796 unique endocrine-related endpoints curated from 3269 published articles. Thereafter, human- and rodent-specific biological endpoint data including interacting genes/proteins, phenotypes, diseases, and adverse outcome pathways (AOPs) were curated from toxicology-relevant databases and systematically integrated with DEDuCTv3.0 to construct a large-scale toxicology knowledge graph for EDCs, termed DEDuCT-KG. DEDuCT-KG was then hosted on a Neo4j database and made easily accessible through a novel interactive user interface. The utility of DEDuCT-KG was demonstrated by exploring potential mechanisms of action associated with obesogenic EDCs within DEDuCTv3.0. Furthermore, the constructed EDC-AOP network, linking 949 EDCs to 381 AOPs within AOP-Wiki, revealed diverse toxicity mechanisms associated with EDCs. Integration with consumer product database and regulatory chemical lists showed that some of these EDCs are present in food contact materials, personal care products, and daily use items, highlighting potential exposure pathways. Overall, all data compiled in this study have been integrated into the DEDuCT webserver, which has been further enhanced to align with FAIR principles. In sum, this study provides a much-needed update to DEDuCT and offers a single point of access to EDC-relevant data to accelerate research and regulation of EDCs.

pharmacology and toxicology↗

bioRxiv2025

Integrative network-based construction of ecologically relevant computational adverse outcome pathways for organic mercury-induced toxicity

Adverse outcome pathways (AOPs) describe mechanisms of toxicity by connecting molecular events with outcomes at higher levels of biological organization. Computational AOPs (cAOPs), constructed using existing toxicological data, can accelerate the early stages of AOP development, which is often a time-consuming and resource-intensive process. In this study, an integrative network-based framework was developed to construct cAOPs, with a particular focus on elucidating the toxicity of organic mercury in fish. First, 124 organic mercury compounds, corresponding fish-specific toxicity endpoints, and proteins were curated from CTD and ECOTOX. Next, molecular docking investigations were performed to determine novel molecular interactions with 16 zebrafish protein orthologs. Subsequently, the toxicity endpoints, including the identified molecular interactions, were standardized and harmonized using data from AOP-Wiki, Gene Ontology, and MeSH. These endpoints were integrated with event relationship information from AOP-Wiki and published literature to construct an organic mercury-associated toxicity network comprising 197 nodes and 243 edges. Thereafter, node- and edge-level filtration criteria were designed based on AOP definitions and utilized to identify biologically relevant pathways within the toxicity network. Further, these pathways were ranked based on their novelty with respect to existing AOPs within AOP-Wiki. Finally, based on extensive evidence from published literature, four top-ranked novel pathways that describe binding with glutathione peroxidase or alterations in metallothionein levels leading to neurologic manifestations or dysbiosis in fish were proposed as cAOPs. Overall, this study presents an integrative network-based framework for constructing cAOPs applicable to diverse contaminants and species, supporting New Approach Methodologies for toxicological risk assessment.

pharmacology and toxicology↗

bioRxiv2025

Sec and Tat mediated secretion safeguards Mycobacterium tuberculosis membrane homeostasis

Protein secretion drives Mycobacterium tuberculosis (Mtb) physiology and pathogenesis, yet a unified picture of the machinery and its role in cell membrane homeostasis is still lacking. By comprehensively curating published evidence, we assembled a systems-level map of Mtb secretion encompassing 92 components and 198 mechanistic reactions across Sec, Tat, and ESX pathways. The secretory components identified were integrated with high-throughput ChIP-Seq and transcriptome datasets to elucidate the regulation of the secretion system. Using CRISPRi, conditional depletion of SecA1 or TatA impaired growth in vitro and survival ex vivo. Quantitative secretome revealed decreased export of SecA1- and TatA-dependent substrates, with enrichment of cytosolic proteins in culture filtrates, indicating increased membrane permeability. Membrane proteomics showed depletion dependent increased metabolic/lipid-degrading proteins and decreased cell-wall/cell-process proteins, consistent with loss of membrane stability. Ultrastructural defects and increased ethidium bromide uptake confirmed impaired membrane integrity. Together, our multi-omics and functional genetics established SecA1 and TatA as essential guardians of Mtb membrane integrity which provided valuable datasets and a framework for secretion-dependent Mtb pathogenesis.

microbiology↗

bioRxiv2025

TICToK: A comprehensive knowledgebase of tattoo ink chemicals and investigation of their associated toxicities and regulations

Tattoos and permanent make-up, involving dermal injection of chemicals, are increasingly popular, with 30-40% of young adults in North America and Europe having at least one tattoo. Recent studies have suggested a possible association between tattooing and health risks, including skin cancers and lymphomas, although no definitive link has yet been established. This study comprehensively catalogs tattoo ink chemicals and investigates their potential adverse effects, addressing the urgent need for greater understanding of tattoo-related health concerns. First, 364 unique tattoo chemicals were identified from various scientific and regulatory sources, with nearly half functioning as pigments. Hazardous chemicals were identified, revealing carcinogens, endocrine disruptors, neurotoxicants, and dermal toxicants. A regulatory analysis based on key EU regulations, including harmonised classifications under Classification, Labelling and Packaging (CLP) regulation, Restriction Entry 75 of the REACH regulation, Cosmetic Products Regulation (CPR), and SVHC candidate list, revealed existing regulatory coverage of tattoo ink chemicals. Curated chemical-disease associations highlighted that some tattoo chemicals are known to cause dermatitis. Further, diverse toxicological information, including experimental results from REACH dossiers, were integrated to construct stressor-AOP network linking 151 chemicals to 362 AOPs, revealing potential carcinogenic mechanisms associated with tattoo ink chemicals. A systems biology approach revealed potential immunomodulatory effects associated with these chemicals. Finally, all findings have been made available through the online database Tattoo Ink Chemicals and associated Toxicities Knowledgebase (TICToK; https://cb.imsc.res.in/tictok), which can support risk assessment and sustainable tattoo practices. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/668261v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@6ce8e3org.highwire.dtl.DTLVardef@2dc5aborg.highwire.dtl.DTLVardef@10c67e6org.highwire.dtl.DTLVardef@1795281_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Simple threshold-based Boolean rules fall short in capturing biological regulatory network dynamics

Among the various frameworks for modeling gene regulatory network (GRN) dynamics, Boolean modeling remains both powerful and accessible. Nevertheless, selecting update rules so that they capture the combinatorial control of various targets remains challenging due to limited quantitative data. Threshold majority rules (TMRs), a subtype of threshold functions (ThFs), update a genes state based on the signed sum of its regulators and its own activity, offering an elegant simplification. However, does the use of TMRs come at the cost of biological realism? Here, we rigorously evaluate the two standard TMR variants regarding their suitability in GRN modeling. We find that they provide limited canalyzation, exhibit discordant bias patterns, and often eliminate self-inhibitions. They are also underrepresented in empirical datasets, the opposite of what is expected for biologically relevant rules. Compared to nested canalyzing functions (NCFs), another class of ThFs but known to be preponderant in empirical datasets, TMRs exhibit heightened complexity and sensitivity to perturbations. At the network level, TMRs frequently fail to recover biological attractors and the associated basin size distributions. Using ensembles of random Boolean networks, we also show that one TMR type drives the network dynamics toward the chaotic regime. These findings invite a thoughtful reevaluation of TMRs as an appropriate logic for modeling GRNs despite their theoretical appeal.

systems biology↗

bioRxiv2025

Node persistence from topological data analysis reveals changes in brain functional connectivity

Large-scale analyses of brain functional connectivity can uncover disruptions in regional activity and connectivity that are commonly associated with neurological disorders or cognitive decline associated with healthy aging. In our study, we employ persistent homology (PH), a prominent tool in topological data analysis (TDA), to investigate changes in resting-state functional connectivity in healthy aging and autism spectrum disorder (ASD). We analyze functional connectivity changes across three distinct scales: (a) global scale (brain-wide changes), (b) mesoscopic scale (resting-state network-level changes), and (c) local scale (region-level changes). At the local scale, we introduce node persistence, a novel and scalable PH-based measure that detects brain regions with significant differences in healthy aging or ASD. Notably, these regions overlap with regions whose non-invasive stimulation improves motor function in the elderly or alleviates ASD symptoms, suggesting the utility of node persistence in identifying clinically relevant brain regions affected by aging and ASD.

neuroscience↗

bioRxiv2025

sCentInDB: A database of essential oil chemical profiles of Indian medicinal plants

Essential oils are complex mixtures of volatile compounds produced by aromatic plants. Due to their odor and therapeutic properties, essential oils are used in personal care, food flavoring and pharmaceutical industries. As a high-value and low-volume organic product, optimizing plant yield and modifying composition by leveraging knowledge on chemical profiles of essential oils can lead to enhanced bioproducts. To this end, we present sCentInDB, a manually curated DataBase of Essential oil Chemical profiles of Medicinal plants of India, which has been built based on information in published literature. sCentInDB is a FAIR-compliant database which compiles information on 554 Indian medicinal plants at the plant part level, encompassing 2170 essential oil profiles, 3420 chemicals, 471 plant-part-therapeutic use associations, 120 plant-part-odor associations, and 218 plant-part-color associations. For the documented essential oils, sCentInDB also compiles extensive metadata including sample location, isolation and analysis methods. Subsequently, an extensive analysis of the chemical space of essential oils documented in sCentInDB was performed. By constructing a chemical similarity network, the chemical space was found to be structurally diverse with terpenoids distributed across the network. Moreover, a comparison of the scaffold diversity in sCentInDB was performed against three other aroma libraries using cyclic system retrieval curves. Altogether, sCentInDB will serve as a valuable resource for researchers working on plant volatiles and employing genetic engineering to enhance oil yield and composition. Further, sCentInDB will aid in establishment of quality standards for essential oils and provide vital insights for therapeutic and perfumery applications. sCentInDB is accessible at https://cb.imsc.res.in/scentindb/.

pharmacology and toxicology↗

bioRxiv2024

Choices of regulatory logic class modulate the dynamical regime in random Boolean networks

Random Boolean networks (RBNs) have been widely explored as models for understanding complex systems, in particular gene regulatory networks (GRNs). Stability (order) and instability (chaos), critical characteristics of any dynamic system, have naturally been a central focus in RBN research. Several measures have previously been introduced to assess stability, e.g., via damage spreading or via statistical properties of attractors such as their number, lengths or basin sizes. Undoubtedly, network topology plays an important role in shaping the dynamics of RBN. Another key factor influencing the dynamics is the Boolean functions (BFs) assigned to each node. In this work, we conduct a systematic investigation to examine the influence of five different classes of BFs on the dynamics of RBNs. By employing various dynamical stability measures, we show that biologically meaningful BFs consistently drive the dynamics toward the stable regime compared to random BFs. This observation holds across networks with varying size, connectivity, and degree distributions. Additionally, we show that the change in the values of our stability measures with increasing connectivity or size of RBNs is different across the BF classes. For most observables, random BFs change the values significantly, pushing the dynamics toward a more chaotic regime with increasing connectivity or network size. In contrast, biologically meaningful BFs show very minimal fluctuations for most stability measures. These findings emphasize the advantage of restricting to biologically meaningful classes in the reconstruction and modeling of biological systems within Boolean framework.

systems biology↗

bioRxiv2024

Computational prediction of cyclotides from Viola odorata as potential inhibitors against the neuraminidase of Streptococcus pneumoniae

Cyclotides are naturally occurring peptides characterized by a cyclic cystine knot, which provides them with exceptional structural stability. In addition to their stability, cyclotides exhibit diverse therapeutic activities including antimicrobial, antiviral and antitumor activities, making them promising candidates in drug discovery. However, computational studies aimed at identifying cyclotide-based inhibitors for infectious diseases remain limited. To this end, we conducted a virtual screening of cyclotides from an Indian medicinal plant Viola odorata to identify potential inhibitors against a bacterial pathogen causing respiratory infections. We compiled a library of 93 cyclotides by retrieving their structures from public domain or predicting them using the AlphaFold server. We then docked these cyclotides against the neuraminidase protein of Streptococcus pneumoniae and analyzed the interacting residues and binding energies to identify top five potential inhibitors namely, kalata S, kalata B1, cycloviolacin O15, vodo L12, and cycloviolacin O36. Thereafter, we performed molecular dynamics simulations of the protein-cyclotide complexes, and observed that the cyclotides remained stable within the complex. Altogether, this study is the first computational effort to identify potential cyclotide inhibitors against bacterial pathogen causing respiratory diseases, which can be further pursued for experimental validation to develop novel therapeutic agents for respiratory infections.

pharmacology and toxicology↗

bioRxiv2024

Network toxicology focused investigation on the impacts of inorganic arsenic and cadmium on human and ecosystem health

Heavy metals like arsenic and cadmium are persistent environmental pollutants that pose serious health risks to humans and ecosystems due to their toxicity, bioaccumulation potential, and frequent presence in consumer products. Network toxicology offers a holistic in silico framework to elucidate the complex biological mechanisms of toxicity, thereby supporting New Approach Methodologies (NAMs) for toxicity assessment. In this study, network toxicological tools were utilized to investigate arsenic- and cadmium-induced toxicities. Toxicity endpoints associated with inorganic arsenic and cadmium compounds were curated from six exposome-relevant databases and mapped to key events (KEs) across adverse outcome pathways (AOPs) cataloged in AOP-Wiki. This led to construction of stressor-AOP networks, revealing 51 AOPs associated with arsenic and 78 with cadmium, and facilitated mechanistic case studies of pathways relevant to human and ecological health. Toxicity concentrations and bioconcentration factors from the ECOTOX database were then used to construct stressor-species networks, that helped identify species that are vulnerable and potentially bioaccumalate these chemicals. Further, the construction of species sensitivity distributions (SSDs) and toxicity-normalized SSDs (SSDn), provided a comparative framework for prioritizing these compounds in risk assessments. Further, integrating SSD data with stressor-species networks identified species groups particularly sensitive to arsenic and cadmium exposure, enhancing these networks utility for ecological risk assessment. The networks and related data generated in this study are freely available for further research at https://cb.imsc.res.in/heavymetaltox/. Overall, this study offers a comprehensive perspective on the toxicological impact of inorganic arsenic and cadmium compounds, supporting a One Health approach to their regulatory and mitigation strategies.

pharmacology and toxicology↗

bioRxiv2024

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

PurposeSingle-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimers, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. MethodsWe have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. ResultsTo showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimers patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets (>11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases. ConclusionWe present scaLR a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.

bioinformatics↗

bioRxiv2024

Network-based investigation of petroleum hydrocarbons-induced ecotoxicological effects and their risk assessment

Petroleum hydrocarbons (PHs) are compounds composed mostly of carbon and hydrogen, originating from crude oil and its derivatives. PHs are primarily released into the environment through the diffusion of oils, resulting from anthropogenic activities like transportation and offshore drilling, and accidental incidents such as oil spills. Once released, these PHs can persist in different ecosystems and cause long-term detrimental ecological impacts. While the hazards associated with such PH contaminations are often assessed by the concentrations of total petroleum hydrocarbons in the environment, studies focusing on the risks associated with individual PHs are limited. Here, we leveraged different network-based frameworks to explore and understand the adverse ecological effects associated with PH exposure. First, we systematically curated a list of 320 PHs from published reports. Next, we integrated biological endpoint data from toxicological databases, and constructed a stressor-centric adverse outcome pathway (AOP) network linking 75 PHs with 177 ecotoxicologically-relevant high confidence AOPs within AOP-Wiki. Further, we relied on stressor-species network constructions, based on reported toxicity concentrations and bioconcentration factors data for 80 PHs and 28 PHs, respectively, and found that crustaceans are documented to be affected by many of these PHs. Finally, we utilized the aquatic toxicity data within ECOTOX to construct species sensitivity distributions for polycyclic aromatic hydrocarbons (PAHs) prioritized by the US EPA, and derived their corresponding hazard concentrations (HC05) that protect 95% of species in the aquatic ecosystem. Overall, this study highlights the importance of using network-based approaches and risk assessment methods to understand the PH-induced toxicities effectively.

pharmacology and toxicology↗

bioRxiv2024

Towards a Health-Associated Core Keystone (HACK) index for the human gut microbiome

Variations in the normal gut microbiome and the existence of context-dependent disease associations have confounded the identification of microbiome markers of health. A reliable indexing of taxa based on their association with host health and microbiome resilience could accelerate development of microbiome-based therapeutics including selection of live biotherapeutics and facilitate microbiome comparisons in diverse study populations. Here we first investigated 196 taxa for their association with three hallmark properties of health and microbiome-resilience, namely prevalence/community-influence in non-diseased subjects, longitudinal stability and host health, using a discovery cohort of 39,926 publicly available adult (> 18 years) gut microbiomes from 127 studies spanning 42 countries and 28 disease conditions (including 9,434 longitudinal samples). We identified 18 species-level-taxa, referred to as Health-Associated Core Keystones (HACKs), with association-strengths in the top 30 percentile for all three properties. We integrated these association-strengths into a single value, the HACK-index, that ranks taxa based on their estimated contribution to both microbiome stability and host health. We then demonstrate the reproducibility of these indices for the taxon associations with the three properties, using a validation cohort of 4,500 gut microbiomes (from 11 studies with various demographics and diseases). Specific consortia of high HACK-index taxa are also associated with positive response to Mediterranean Diet interventions and Immuno-Checkpoint-Inhibitor therapies. We finally identify the distinguishing microbial genomic functions associated with high HACK-indices/HACK-taxa that can be investigated using targeted mechanistic studies to identify microbiome effectors of improved health. The availability of HACK-indices provides a rational basis for microbiome comparisons and will facilitate the selection and design of microbiome-based therapeutics.

microbiology↗