Biology subjects

Kumar, N.

Publications and source records attributed to Kumar, N..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Pseudomonas aeruginosa-mediated cardiac dysfunction is driven by extracellular vesicles released during infection

Pseudomonas aeruginosa (P.a.) is a gram-negative, opportunistic bacterium abundantly present in the environment. Often P.a. infections cause severe pneumonia, if left untreated. Surprisingly, up to 30% of patients admitted to the hospital for community- acquired pneumonia develop adverse cardiovascular complications such as myocardial infarction, arrhythmia, left ventricular dysfunction, and heart failure. However, the underlying mechanism of infection-mediated cardiac dysfunction is not yet known. Recently, we demonstrated that P.a. infection of the lungs led to severe cardiac electrical abnormalities and left ventricular dysfunction with limited P.a. dissemination to the heart tissue. To understand the mechanism of cardiac dysfunction during P.a. infection, we utilized both in vitro and in vivo models. Our results revealed that inflammatory cytokines contribute but are not solely responsible for severe contractile dysfunction in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Instead, exposure of hiPSC-CMs with conditioned media from P.a. infected human monocyte-derived macrophages (hMDMs) was sufficient to cause severe contractile dysfunction and arrhythmia in hiPSC-CMs. Specifically, exosomes released from infected hMDMs and bacterial outer membrane vesicles (OMVs) are the major drivers of cardiomyocyte contractile dysfunction. By using LC-MS/MS, we identified bacterial proteins, including toxins that are packaged in the exosomes and OMVs, which are responsible for contractile dysfunction. Furthermore, we demonstrated that systemic delivery of bacterial OMVs to mice caused severe cardiac dysfunction, mimicking the natural bacterial infection. In summary, we conclude that OMVs released during infection enter circulation and drive cardiac dysfunction.

immunology↗

bioRxiv2024

Task relevance selectively modulates sensorimotor adaptation in the presence of multiple prediction errors.

Adaptation to consistently occurring sensorimotor errors is considered obligatory in nature. We probed the robustness of this finding by asking if humans can selectively attenuate adaptation based on the task-relevance of error signals. Subjects made planar reaches to three different targets: an arc (Experiment 1), a bar (Experiment 2), and a point (Experiment 3). During the reach, perturbations in extent (visuomotor gain), direction (visuomotor rotation) or both simultaneously were employed. In Experiment 1, subjects showed robust adaptation to the rotation when reaching to the arc even though the presence of this perturbation was irrelevant for achievement of the task goal. Interestingly however, rotation adaptation was strongly attenuated when it was presented simultaneously with a task-relevant gain perturbation. In Experiment 2, which involved reaches to the bar, again, subjects successfully adapted to the task-irrelevant gain perturbation when it occurred in isolation. However, adaptation was attenuated when the gain co-occurred with a task-relevant rotation. Experiment 3 revealed that the attenuation observed in the first two experiments was not due to an inability to adapt to co-occurring rotation and gain perturbations. Collectively, our results suggest that the sensorimotor system selectively tunes learning in the presence of multiple error signals, a finding that can potentially be explained by a biased competition mechanism. That is, given limited processing capacity, a salient attribute - the relevance of the error to the task goal in this case - is prioritized for processing and drives subsequent adaptive changes in motor output. NEW AND NOTEWORTHYThe motor system continuously uses error feedback to recalibrate movements in response to changes in body and environmental conditions. Such error-based adaptation is thought to be obligatory, occurring whenever error signals are present, and even if the learning interferes with achievement of the task goal. Contrary to this classical view, we demonstrate selective modulation of motor adaptation in the presence of multiple error signals based on their task-relevance.

neuroscience↗

bioRxiv2024

PfPPM2 signalling regulates asexual division and sexual conversion of human malaria parasite Plasmodium falciparum

Malaria parasite transits through distinct developmental stages during its life cycle in the human and mosquito host, which includes unique asynchronous division in the erythrocytes. The switch from its asexual stage to sexual forms, which is critical for disease transmission, is intricately regulated but signalling pathways involved in this process have remained unknown. In the present study, we report a novel signalling pathway involving Protein Phosphatase PfPPM2, which regulates asexual division of the parasite as well as its conversion to sexual forms. Phosphoproteomics revealed that PfPPM2 may regulate the phosphorylation of key proteins involved in chromatin remodelling and protein translation. One of the key PfPPM2-targets that emerged from these studies was Heterochromatin Protein 1 (HP1), a regulator of heritable gene silencing which contributes to both mitotic proliferation as well as sexual commitment of the parasite. We demonstrate that PfPPM2 promotes sexual conversion by regulating the interaction between HP1, H3K9me3 and chromatin and it achieves this by dephosphorylating S33 of HP1. Regulation of HP1 and Histone H3 by PfPPM2 may also contribute to division. In addition, PfPPM2 also regulates protein synthesis in the parasite by repressing the phosphorylation of initiation factor eIF2, which is likely to contribute to parasite division and possibly sexual differentiation.

microbiology↗

bioRxiv2024

Designing of thermostable proteins with a desired melting temperature

1.The stability of proteins at higher temperatures is crucial for its functionality that is measured by their melting temperature (Tm). The Tm is the temperature at which 50% of the protein loses its native structure and activity. Existing methods for predicting Tm have two major limitations: first, they are often trained on redundant proteins, and second, they do not allow users to design proteins with the desired Tm. To address these limitations, we developed a regression method for predicting the Tm value of proteins using 17,312 non-redundant proteins, where no two proteins are more than 40% similar. We used 80% of the data for training and testing; remaining 20% of the data for validation. Initially, we developed a machine learning model using standard features from protein sequences. Our best model, developed using Shannon entropy for all residues, achieved the highest Pearson correlation of 0.80 with an R{superscript 2} of 0.63 between the predicted and actual Tm of proteins on the validation dataset. Next, we fine-tuned large language models (e.g., ProtBert, ProtGPT2, ProtT5) on our training dataset and generated embeddings. These embeddings have been used for developing machine learning models. Our best model, developed using ProtBert embeddings, achieved a maximum correlation of 0.89 with an R{superscript 2} of 0.80 on the validation dataset. Finally, we developed an ensemble method that combines standard protein features and embeddings. One of the aims of the study is to assist the scientific community in the design of targeted melting temperatures. We created a user-friendly web server and a python package for predicting and designing thermostable proteins. Our standalone software can be used to screen thermostable proteins in genomes and metagenomes. We demonstrated the application of PPTstab in identifying thermostable proteins in different organisms from their genomes, the model and data is available at: https://webs.iiitd.edu.in/raghava/pptstab. HighlightsO_LIPrediction of melting temperature (Tm) on non-redundant proteins C_LIO_LIMachine learning models based on sequence composition and ProtBert embeddings C_LIO_LIA Webserver for predicting Tm and designing thermostable proteins C_LI

bioinformatics↗

bioRxiv2024

Additive and context-dependent effects of developmental and adult diet on life-history traits and their associations in outbred Drosophila melanogaster population

Life-history traits such as body size, reproduction, survival, and stress resistance are fundamental to an organisms fitness and are highly influenced by nutritional environments across life stages. In this study, we employed a full factorial experimental design to investigate the effects of isocaloric diets (diets with equal caloric content but differing macronutrient composition) on key life-history traits in an outbred Drosophila melanogaster population. Our results demonstrated significant diet-induced plasticity, with male wing length (a proxy for body size) being influenced by the developmental diet; males reared on carbohydrate-rich developmental diets had larger wings as adults. Fertility increased with protein-rich diets at both developmental and adult stages, reaffirming the critical role of dietary protein in enhancing reproductive success. Lifespan exhibited sexually dimorphic responses to diet: carbohydrate-rich developmental diets extended male lifespan, while carbohydrate-rich adult diets reduced lifespan in both sexes. Stress resistance traits, including starvation and desiccation resistance, were unaffected by developmental diets but were influenced by adult diets, with carbohydrate-rich adult diets enhancing survival under both stress conditions in males and females. Importantly, while most traits exhibited additive effects of nutrition across life stages, a marginal interaction for male starvation resistance suggests that developmental and adult diets can interact in a trait- and sex-specific manner. Moreover, associations between dietary effects on life-history traits were context-dependent, driven primarily by adult diets and varying by sex. These findings emphasize the profound role of stage-specific nutritional environments in modulating life-history traits and their correlations, offering valuable insights into how organisms may adapt to changing ecological conditions and highlighting the importance of considering both developmental and adult dietary contexts in evolutionary studies.

evolutionary biology↗

bioRxiv2024

ROLE OF WORKING MEMORY IN INTERLIMB GENERALIZATION OF NEWLY LEARNED SKILLS

Newly acquired skill memory can generalize/transfer to the untrained arm. Such interlimb generalization of a learned skill has been shown to be symmetric in nature and is thought to be mediated by cognitive processes that emerge during skill learning. However, it is unknown whether engaging in other cognitively demanding tasks following skill acquisition can influence skill generalization. Our research goal was to uncover how a secondary task, involving working memory, interacts with a newly formed skill memory and influences subsequent interlimb generalization. To test this idea, we conducted a set of three experiments by recruiting right-handed young healthy individuals (N=92) who learned a novel motor skill (long or short training on a skilled reaching task) followed by performing a working memory or control task with the right arm. Finally, all individuals were tested for immediate or delayed (after 24 hours) interlimb skill generalization to the untrained left arm. We found significant immediate as well as delayed generalization in individuals who received long training on the motor skill task, irrespective of whether they performed working memory or control task. On the other hand, performing the working memory but not control task following short skill training impaired generalization when the untrained arm was tested 24 hours later. These findings indicate that short training reflecting early stages of skill learning and the subsequent skill memory stabilization are dependent on working memory such that the underlying neural interactions mediating these processes can have implications for skill generalization. NEW AND NOTEWORTHYSkill learning is deemed successful if it can generalize to untrained conditions. Despite the potential clinical relevance of interlimb skill generalization for motor rehabilitation, such as in case of unilateral stroke, the underlying mechanisms that can influence such generalization are not fully understood. Specifically, whether interlimb skill memory generalization can be impacted by another memory system is unknown. Here, we show that engaging in a working memory task following short skill training is detrimental for interlimb skill generalization, and such interference caused by working memory is not evident when the skill training is longer. These behavioral findings bridge the link between traditionally distinct memory systems, i.e., procedural skill memory and working memory.

neuroscience↗

bioRxiv2024

Genome structural variants shape adaptive success of an invasive urban malaria vector Anopheles stephensi

Global changes are associated with the emergence of several invasive species. However, the genomic determinants of the adaptive success of an invasive species in a new environment remain poorly understood. Genomic structural variants (SVs), consisting of copy number variants, play an important role in adaptation. SVs often cause large adaptive shifts in ecologically important traits, which makes SVs compelling candidates for driving rapid adaptations to environmental changes, which is critical to invasive success. To address this problem, we investigated the role SVs play in the adaptive success of Anopheles stephensi, a primary vector of urban malaria in South Asia and an invasive malaria vector in several South Asian islands and Africa. We collected whole genome sequencing data from 115 mosquitoes from invasive island populations and four locations from mainland India, an ancestral range for the species. We identified 2,988 duplication copy number variants and 16,038 deletions in these strains, with [~]50% overlapping genes. SVs are enriched in genomic regions with signatures of selective sweeps in the mainland and invasive island populations, implying a putative adaptive role of SVs. Nearly all high-frequency SVs, including the candidate adaptive variants, in the invasive island populations are present on the mainland, suggesting a major contribution of existing variation to the success of the island populations. Among the candidate adaptive SVs, three duplications involving toxin-resistance genes evolved, likely due to the widespread application of insecticides in India since the 1950s. We also identify two SVs associated with the adaptation of An. stephensi larvae to brackish water in the island and two coastal mainland populations, where the mutations likely originated. Our results suggest that existing SVs play a vital role in the evolutionary success of An. stephensi in new environmental conditions.

evolutionary biology↗

bioRxiv2024

Prediction of Hemolytic Peptides and their Hemolytic Concentration (HC50)

Several peptide-based drugs fail in clinical trials due to their toxicity or hemolytic activity against red blood cells (RBCs). Existing methods predict hemolytic peptides but not the concentration (HC50) required to lyse 50% of RBCs. In this study, we developed a classification model and regression model to identify and quantify the hemolytic activity of peptides. Our models were trained and validated on 1924 peptides with experimentally determined HC50 against mammalian RBCs. Analysis indicates that hydrophobic and positively charged residues were associated with higher hemolytic activity. Our classification models achieved a maximum AUC of 0.909 using a hybrid model of ESM-2 and a motif-based approach. Regression models using compositional features achieved R of 0.739 with R{superscript 2} of 0.543. Our models outperform existing methods and are implemented in the web-based platform HemoPI2 and standalone software for designing hemolytic peptides with desired HC50 values (http://webs.iiitd.edu.in/raghava/hemopi2/). HighlightsO_LIDeveloped classification and regression models to predict hemolytic activity and HC50 values of peptides. C_LIO_LIA hybrid model combining machine learning and motif prediction excels in accuracy. C_LIO_LIBenchmarking of the existing classification methods on independent datasets. C_LIO_LIWeb server, standalone software, and pip package for hemolytic activity prediction of peptides/proteins. C_LI

bioinformatics↗

bioRxiv2024

Prediction of plant resistance proteins using alignment-based and alignment-free approaches

Plant Disease Resistance (PDR) proteins are critical in identifying and killing plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as BLAST for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning-based methods using compositional features of proteins. Our machine learning-based model, developed using compositional features of proteins, achieved a maximum performance AUROC of 0.92. The performance of our model improved significantly from AUROC of 0.92 to 0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best machine learning model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, "PlantDRPpred" (https://webs.iiitd.edu.in/raghava/plantdrppred). HighlightsO_LIDevelopment of a Machine-learning model for resistance protein prediction. C_LIO_LIUsed alignment-based and alignment-free ensemble methods. C_LIO_LIWeb server development and standalone package. C_LIO_LIPrediction and design of PDR proteins. C_LI

bioinformatics↗

bioRxiv2024

Prediction of inhibitory peptides against E. coli with desired MIC value

In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we trained our models on 3,143 peptides and validated them on 786 peptides whose MIC values have been determined experimentally against Escherichia coli (E. coli). The correlational analysis reveals that the Composition Enhanced Transition and Distribution (CeTD) attributes strongly correlate with MIC values. We initially employed the similarity search strategy utilizing BLAST to estimate MIC values of peptides but found it inadequate for prediction. Next, we developed machine learning techniques-based regression models using a wide range of features, including peptide composition, binary profile, and embeddings of large language models. We implemented feature selection techniques like minimum Redundancy Maximum Relevance (mRMR) to select the best relevant features for developing prediction models. Our Random forest-based regressor, based on selected features, achieved a correlation coefficient (R) of 0.78, R-squared (R{superscript 2}) of 0.59, and a root mean squared error (RMSE) of 0.53 on the validation dataset. Our best model outperforms the existing methods when benchmarked on an independent dataset of 498 inhibitory peptides of E. coli. One of the major features of the web-based platform EIPpred developed in this study is that it allows users to identify or design peptides that can inhibit E. coli with the desired MIC value (https://webs.iiitd.edu.in/raghava/eippred). HighlightsO_LIPrediction of MIC value of peptides against E.coli. C_LIO_LIAn independent dataset was generated for comparison. C_LIO_LIFeature selection using the mRMR method. C_LIO_LIA regressor method for designing novel inhibitory peptides. C_LIO_LIA web server and standalone package for predicting the inhibitory activity of peptides. C_LI

bioinformatics↗

bioRxiv2024

Dicarbonyl stress enhances tumor intravasation

Metastasis of cancer is a multi-step process that involves the migration of transformed cells from their native organ into a vascular channel, followed by their dissemination to prospective sites of colonization. The entry of tumor cells into blood or lymph, known as intravasation involves their breaching the stromal and endothelial extracellular matrix (ECM) and the endothelial barriers. How the kinetics of these cell-ECM interactions are confounded by chronic inflammatory stresses seen in comorbid risk factors of cancer such as diabetes and aging remain ill-investigated. Here, we construct and deploy a histopathology-motivated, imaging-tractable, microfluidic multi-organ-on-chip platform, that seamlessly integrates two tissue environments: that of a breast tumor and a vascular channel, to study the problem. The former comprises invasive triple-negative MDA-MB-231 breast cancer cells embedded within a three-dimensional fibrillar Collagen I milieu. The latter consists of a monolayer of TeloHAEC, immortalized human aortic endothelial cells arranged on laminin-rich basement membrane ECM, both of which concentrically line a hollow channel, wherein unidirectional fluid flows are implemented. The chip showcases the complexity of intravasation, wherein tumor cells and endothelia cooperate to form anastomotic structures. The formation of such structures is regulated by fluid flow in the vascular channel and is associated with cancer cell migration and entry into the vascular channel. Disseminated cancer cells are observed to enter, get adhered within, and flow through the vascular channel. Exposure to methylglyoxal (MG), a mediator of dicarbonyl stress associated with diabetic circulatory milieu, leads to greater cancer cell intravasation and flow through the vascular channel. This could be driven not just by MG-induced endothelial senescence and shedding, but also by the effect of MG on the chip ECM: we demonstrate it can degrade basement membrane and pathologically crosslink Collagen I, diminishing their cell adhesiveness. Our results thus show how dicarbonyl stress may attenuate homoeostatic barriers to cancer intravasation, exacerbating metastasis.

bioengineering↗

bioRxiv2024

Rosettes in a matrix: Predicting spatial variation in density of a large felid in a forest-production mosaic

Large carnivores are keystone for ecosystems and flagships for conservation efforts but face severe threats globally. Protected Areas are vital for the conservation of these charismatic species along with a host of ecological processes. However, the extent and scope of protected areas for conservation of all threatened species is limited, especially in the global south. Considering larger landscapes that can be compatible with large carnivore conservation goals is an alternative approach to ensure their persistence. This study explores the potential of multi-use landscapes for the persistence of a globally threatened large felid, the leopard. This study investigated the spatial variability of leopard densities across a land-use gradient using spatially explicit capture-recapture framework in a tea-plantation dominated forest-production landscape mosaic. While the density of leopards in this landscape was estimated to be 7.96 {+/-} 1.56 (SE) per 100 km2, significant (p=0.048, t=2.02, df=61) differences in estimates were observed between tea-plantations (11.53 {+/-} 2.72 (SE) leopards per 100 km2) and the forested habitats (4.67 {+/-} 2.07 (SE) per 100 km2). Densities between tea plantations and Protected Areas (a subset of the forested habitat) were found to be comparable (9.19 {+/-} 4.55 (SE) per 100 km2). The study posits that conservation-compatible land use in landscapes shared with people can host a higher density of large felids than forested areas and that conservation planning needs to move beyond the dominant PA-centric paradigm. The study also reinforces the importance of multi-use landscapes for wildlife conservation, especially for an adaptable large felid.

ecology↗

bioRxiv2024

Comparison of gene-by-gene and genome-wide short nucleotide sequence based approaches to define the global population structure of Streptococcus pneumoniae

Defining the population structure of a pathogen is a key part of epidemiology, as genomically related isolates are likely to share key clinical features such as antimicrobial resistance profiles and invasiveness. Multiple different methods are currently used to cluster together closely- related genomes, potentially leading to inconsistency between studies. Here, we use a global dataset of 26,306 S. pneumoniae genomes to compare four clustering methods: gene-by- gene seven-locus multi-locus sequencing typing (MLST), core genome MLST (cgMLST)- based hierarchical clustering (HierCC) assignments, Life Identification Number (LIN) barcoding, and k-mer-based PopPUNK clustering (known as GPSCs in this species). We compare the clustering results with phylogenetic and pan-genome analyses to assess their relationship with genome diversity and evolution, as we would expect a good clustering method to form a single monophyletic cluster that has high within-cluster similarity of genomic content. We show that the four methods are generally able to accurately reflect the population structure based on these metrics, and that the methods were broadly consistent with each other. We investigated further to study the discrepancies in clusters. The greatest concordance was seen between LIN barcoding and HierCC (Adjusted Mutual Information Score = 0.950), which was expected given that both methods utilise cgMLST, but have different methods for defining an individual cluster and different core genome schema. However, the existence of differences between the two methods show that the selection of a core genome schema can introduce inconsistencies between studies. GPSC and HierCC assignments were also highly concordant (AMI = 0.946), showing that k-mer based methods which use the whole genome and do not require the careful selection of a core genome schema are just as effective at representing the population structure. Additionally, where there were differences in clustering between these methods, this could be explained by differences in the accessory genome that were not identified in cgMLST. We conclude that for S. pneumoniae, standardised and stable nomenclature is important as the number of genomes available expands. Furthermore, the research community should transition away from seven- locus MLST, and cgMLST, GPSC, and LIN assignments should be used more widely. However, to allow for easy comparison between studies and to make previous literature relevant, the reporting of multiple clustering names should be standardised within research. Data summaryGenome sequences are deposited in the European Nucleotide Archive (ENA); accession numbers. Metadata of the pneumococcal isolates in this study have been submitted as a supplementary file and are also available on the Monocle Database available at https://data.monocle.sanger.ac.uk/. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Impact StatementUsing a global dataset of S. pneumoniae genomes allows us to thoroughly observe and analyse discrepancies between different clustering methods. Whilst all methods in this study are used to cluster S. pneumoniae genomes, no study has yet thoroughly compared the clustering results and discrepancies. This work summarises the strengths and weaknesses of the different methods and highlights the need for consistency between studies.

genomics↗

bioRxiv2024

Effect of developmental and adult diet composition on reproductive aging in Drosophila melanogaster

Diet significantly affects reproductive outcomes across species, yet the precise effects of macronutrient compositions beyond caloric intake on reproductive aging are understudied. Existing literature presents conflicting views on the fertility impacts of nutrient-rich versus nutrient-poor developmental diets, underscoring a notable research gap. This study addresses these gaps by examining effects of isocaloric diets with varied protein-to-carbohydrate ratios during both developmental and adult stages on reproductive aging of a large, outbred Drosophila melanogaster population (n = [~]2100). Our results clearly demonstrate an age-dependent dietary impact on reproductive output, initially dominated by the developmental diet, then by a combination of developmental and adult diets in early to mid-life, and ultimately by the adult diet in later life. Importantly, we found that the effects of developmental and adult diets on reproductive output are independent, with no significant interaction. Further investigations into the mechanisms revealed that the effect of developmental diet on fecundity is regulated via ovarioles formation and vitellogenesis; while, the effect of adult diet on fecundity is mostly regulated only via vitellogenesis. These insights resolve disputes in the literature about dietary impacts on fertility and offer valuable perspectives for optimizing fertility strategies in improving public health and conservation efforts in this changing world. HighlightsO_LIEffect of developmental and adult diet composition on reproduction is age-dependent C_LIO_LIDevelopmental diet affects early-life; adult diet late-life; and both affect mid-life C_LIO_LIBut the effect of developmental and adult diets do not interact with each other C_LIO_LIDevelopmental diet regulates reproduction via ovarioles formation and vitellogenesis C_LIO_LIWhereas, adult diet regulates reproduction via differential vitellogenesis across age C_LI

ecology↗

bioRxiv2024

Hemispheric specialization of functions are tuned by conduction velocities of neuronal propagation in large-scale brain networks

Analogous to the notion of pleiotropy where one gene can influence two or three unrelated traits, shared structural connectome encompassing both hemispheres of human brain gets functionally segregated along left vs right hemispheres during speech and melody processing, respectively. Here, the underlying neural mechanism is explored with a combined empirical and computational approach wherein, context-specific causal outflow of information emerging from primary auditory cortices (PAC) is shown to be guiding the hemispheric specialization of speech and melody processing while human volunteers listened to naturalistic music while their electroencephalogram (EEG) were recorded. Using a detailed whole-brain connectome model guided by diffusion MRI, individual-specific functional lateralization were predicted at cortical sources representing an extended auditory processing network. High levels of accuracy in prediction can be achieved after optimizing conduction velocity - controller of transmission delays of neural information processing. Thus, a tuning-by-delay mechanism is presented, which when triggered by the spectro-temporal complexity of the task context, guides the geometry of lateralization.

neuroscience↗

bioRxiv2024

Prediction of anti-freezing proteins from their evolutionary profile

1.Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates proposed and existing methods on an independent dataset containing 81 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition-based protein features and achieved a peak AUC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST-based similarity and motif-based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine-learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user-friendly web server with a standalone package named "AFPropred" was developed (https://webs.iiitd.edu.in/raghava/afpropred). HighlightsO_LIPrediction of antifreeze proteins with high precision C_LIO_LIEvaluation of prediction models on an independent dataset C_LIO_LIMachine learning based models using sequence composition C_LIO_LIEvolutionary information based prediction models C_LIO_LIA webserver for predicting, scanning, and designing AFPs. C_LI Authors BiographyO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LINisha Bajiya is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as a postdoctoral visiting fellow Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2024

Development of a new genotype-phenotype linked antibody screening system

Antibodies are powerful tools for the therapy and diagnosis of various diseases. In addition to conventional hybridoma-based screening, recombinant antibody-based screening has become a common choice; however, its application is hampered by two factors: 1) screening starts after Ig gene cloning and recombinant antibody production only, and 2) the antibody is composed of paired chains, heavy and light, commonly expressed by two independent expression vectors. Here, we introduce a method for the rapid screening of recombinant monoclonal antibodies by establishing a Golden Gate-based dual-expression vector and in-vivo expression of membrane-bound antibodies. Using this system, we demonstrate the rapid isolation of influenza cross-reactive antibodies with high affinity from immunized mice within 7 days. This system is particularly useful for isolating therapeutic or diagnostic antibodies, e.g., during foreseen pandemics. Impact StatementA Golden Gate-based dual-expression vector enables rapid screening, which facilitates efficient isolation of high-affinity cross-reactive antibodies for therapeutic or diagnostic use and provides a crucial advance for pandemic preparedness.

bioengineering↗

bioRxiv2024

Single nuclei multiomics reveals the drought-driven gene regulatory atlas in Arabidopsis

The regulation of gene expression in plant responses to drought has been thoroughly investigated in previous studies. Despite this, a detailed understanding of the cell type-specific regulatory mechanisms, encompassing multi-layered biological processes, is lacking. In this study, we report the use of single-nucleus multiomic analysis in Arabidopsis seedlings in response to drought stress. Our single-nuclei RNA (snRNA) analysis delineated 14 distinct clusters representing major root and shoot cell types and discovered new cell type-specific drought markers. Integration of snRNA with single-nuclei ATAC (snATAC) data in leaf epidermis, root endodermis, and guard cells revealed accessible chromatin regions (ACRs)-linked genes predominantly enriched in pathways responsive to drought, heat, and light. Motif enrichment analysis and gene regulatory network (GRN) inference highlighted key transcription factors (TFs) and regulatory networks related to ethylene signaling pathways in endodermis as well as circadian rhythms in both endodermis and guard cells. Pseudotime analysis identified critical transcriptomic progression from metabolic process to stress response within three cell types. Overall, this study elucidates drought-related regulatory mechanisms in Arabidopsis at single-cell resolution, providing valuable insights into the fundamental regulatory events involved in stress responses. It also serves as a reference for future single-cell multiomic investigations in crop plants. One Sentence SummarySingle cell multiomic analysis under drought stress

plant biology↗