Biology subjects

Banerjee, S.

Publications and source records attributed to Banerjee, S..

At least 217 records · Page 12Linked to original sources

bioRxiv2021

The novel leishmanial Copper P-type ATPase plays a vital role in intracellular parasite survival

Copper is essential for all life forms; however in excess it is extremely toxic. Toxic properties of copper are utilized by hosts against various pathogenic invasions. Leishmania, in its both free-living and intracellular forms, was found to exhibit appreciable tolerance towards copper-stress. To determine the mechanism of copper-stress evasion employed by Leishmania we identified and characterized the hitherto unknown Copper-ATPase in Leishmania major and determined its role in parasites survival in host macrophage cells. L. major Cu-ATPase, LmATP7, exhibits high homology with its orthologues at multiple conserved motifs. In promastigotes, LmATP7 localized to the plasma membrane with a fraction in intracellular puncta. Upon copper treatment, LmATP7 expression increases few folds. LmATP7 is capable of complementing copper transport in Cu-ATPase-{Delta} yeast strain. Promastigotes overexpressing LmATP7 exhibits higher survival upon copper stress indicating efficacious copper export compared to wild type and heterozygous LmATP7 knock-out parasites. We explored macrophage-Leishmanial interaction with respect to copper stress subjected by the host upon parasite and the parasites reciprocating response thereon to evade the stress. The Copper-P-type-ATPases ATP7A/7B serve as major copper exporters in mammals that maintain cellular copper levels. We found that Leishmania infection triggers ATP7A upregulation in macrophages. Additionally, as part of host response, ATP7A traffics from trans-Golgi network and transports copper to the endosomal and endolysosomal compartments harbouring the Leishmania amastigotes. Finally, we show LmATP7 overexpression in this parasite increased amastigote survivability within infected macrophages whereas knocking it down reduces that drastically, establishing its role in combating host-induced copper stress.

cell biology↗

bioRxiv2020

Deciphering colchicine like actions of clerodin in terms of microtubule destabilization based mitotic abnormalities, G2/M-phase arrest, and plant polyploidy

Clerodin (C24H34O7), a clerodane diterpenoid, is a bitter principle of Clerodendrum viscosum. The present study aimed to decipher colchicine-like actions of clerodin in terms of microtubule destabilization based mitotic abnormalities, G2-M arrest, and plant polyploidy. Purified clerodin showed increased metaphase frequency in Human Peripheral Blood Lymphocytes (HPBLs), Human Embryonic Kidney cells (HEK-293), and Allium cepa root apical meristem cells. Both squashed slide of the onion root tip and flow cytometric analysis of radish protoplast revealed a significantly increased frequency of polyploid cells. Flow cytometric analysis showed an increase in frequencies of G2-M in MCF-7 cells from 6.10 to 16.25% after clerodin (200g/mL) treatment for 24 h. Confocal microscopy imaging of tubulin in clerodin-treated MCF-7 cells revealed microtubule destabilization. Molecular docking and LIGPLOT analysis indicate that clerodin interact in the colchicine binding site, including, single hydrogen bond with Asn 101 of -tubulin. In summary, our experimental data revealed that clerodin has metaphase arresting, microtubule destabilization, and polyploidy inducing ability similar to colchicine. Molecular docking analysis revealed for the first time that clerodin and colchicine interact at the common site of tubulin residue indicating a common mechanism of action. The results also indicate similar cytotoxic potentialities of both clerodin and colchicine even though they belong to different chemical groups. Thus, clerodin may be used in place of colchicine as a plant polyploidy inducing agent in plant breeding programs in Agriculture.

cell biology↗

bioRxiv2020

Hindbrain neuropore tissue geometry determines asymmetric cell-mediated closure dynamics

Gap closure is a common morphogenetic process. In mammals, failure to close the embryonic hindbrain neuropore (HNP) gap causes fatal anencephaly. We observed that surface ectoderm cells surrounding the mouse HNP assemble high-tension actomyosin purse-strings at their leading edge and establish the initial contacts across the embryonic midline. The HNP gap closes asymmetrically, faster from its rostral than caudal extreme, while maintaining an elongated aspect ratio. Cell-based physical modelling identifies two closure mechanisms sufficient to describe tissue-level HNP closure dynamics; purse-string contraction and directional cell crawling. Combining both closure mechanisms hastens gap closure and produces a constant rate of gap shortening. Purse-string contraction reduces, whereas crawling increases gap aspect ratio, and their combination maintains it. Closure rate asymmetry can be explained by embryo tissue geometry, namely a narrower rostral gap apex. At the cellular level, our model predicts highly directional cell migration with a constant rate of cells leaving the HNP rim. These behaviours are reproducibly live-imaged in mouse embryos. Thus, mammalian embryos coordinate cellular and tissue-level mechanics to achieve this critical gap closure event.

biophysics↗

bioRxiv2020

A multimodal cell census and atlas of the mammalian primary motor cortex

We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

neuroscience↗

bioRxiv2020

Better expression profile of CD8 and CD4 gene in uterus of pregnant ewe in comparison to non-pregnant - a novel report

CD8 and CD4 T cells play a central role in the immune response to viruses and intracellular pathogens as well as functions for the maintenance of both the mother and fetus. The present study was conducted to explore the differential gene expression profile for CD8 and CD4 present in the uterus with reference to the gravid and non-gravid Garole sheep and confirmation through immuno histochemical studies. Better CD8 and CD4 gene expression was observed in the mid uterus of pregnant ewes compared to that of non-pregnant ewes, where CD8 expression was better to that of CD4. Gene expression profiling of CD8 and CD4 are reported here for the first time in sheep. CD8 and CD4 expression may be regarded as the useful factor for maintenance of pregnancy. The current observations demonstrate that during pregnancy in ewe the immune system may respond to changes in the maternal environment to maintain the size and function of the CD8 and CD4 T-cell compartment. CD8 and CD4 expression may be employed as marker for pregnancy detection in sheep, which remains always a challenge for sheep breeders.

immunology↗

bioRxiv2020

Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning

Theoretical and empirical advances have revealed the importance of biodiversity for stabilizing ecosystem functions through time. Yet despite the global degradation of soils, how the loss of soil microbial diversity can de-stabilizes ecosystem functioning is unknown. Here we experimentally quantified the contribution diversity and the temporal dynamics in the composition of soil microbial communities to the temporal stability of four key ecosystem functions related to nutrient and carbon cycling. Soil microbial diversity loss reduced the temporal stability of all ecosystem functions and was particularly strong when over 50% of microbial taxa were lost. The stabilizing effect of soil biodiversity was linked to asynchrony among microbial taxa whereby different soil fungi and bacteria were associated with different ecosystem functions at different times. Our results emphasize the need to conserve soil biodiversity in order to ensure the reliable provisioning of multiple ecosystems functions that soils provide to society.

ecology↗

bioRxiv2020

Cellular Anatomy of the Mouse Primary Motor Cortex

An essential step toward understanding brain function is to establish a cellular-resolution structural framework upon which multi-scale and multi-modal information spanning molecules, cells, circuits and systems can be integrated and interpreted. Here, through a collaborative effort from the Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based description of one brain structure - the primary motor cortex upper limb area (MOp-ul) of the mouse. Applying state-of-the-art labeling, imaging, computational, and neuroinformatics tools, we delineated the MOp-ul within the Mouse Brain 3D Common Coordinate Framework (CCF). We defined over two dozen MOp-ul projection neuron (PN) types by their anterograde targets; the spatial distribution of their somata defines 11 cortical sublayers, a significant refinement of the classic notion of cortical laminar organization. We further combine multiple complementary tracing methods (classic tract tracing, cell type-based anterograde, retrograde, and transsynaptic viral tracing, high-throughput BARseq, and complete single cell reconstruction) to systematically chart cell type-based MOp input-output streams. As PNs link distant brain regions at synapses as well as host cellular gene expression, our construction of a PN type resolution MOp-ul wiring diagram will facilitate an integrated analysis of motor control circuitry across the molecular, cellular, and systems levels. This work further provides a roadmap towards a cellular resolution description of mammalian brain architecture.

neuroscience↗

bioRxiv2020

RIG-I has a role in immunity against Haemonchus contortus, a gastrointestinal parasite in Ovis aries- a novel report.

RIG-I is associated to the DExD/H box RNA Helicases. It is a Pattern Recognition Receptor (PRR), playing a crucial role in the system and is a germ line encoded host sensor to perceive Pathogen Associated Molecular Patterns or PAMPs. So far reports were available for the role of RIG-I in antiviral immunity. This is the first report we have documented the role of RIG-I in parasitic immunity. Haemonchus contortus is a deadly parasite affecting sheep industry which has a tremendous economic importance and the parasite reported to be prevalent in the hot and humid agroclimatic region. We had characterized RIG-I gene in sheep (Ovis aries) and identified the important domains or binding site with Haemonchus contortus through in silico studies. Differential mRNA expression analysis revealed upregulation of RIG-I gene in abomassum of infected sheep compared to that of healthy sheep, further confirming the findings. Thus it is evident that in infected sheep, expression of RIG-I is triggered for binding to more pathogen (Haemonchus contortus). Genetic similar studies with human and other livestock species were conducted to reveal that sheep may be efficiently used a model organism for studying the role of RIG-I in antiparasitic immunity of human.

genomics↗

bioRxiv2020

NGPINT: A Next-generation protein-protein interaction software

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate cDNA libraries represent an alternative approach that optimizes scale, cost, and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and recognized all validated interactions. NGPINT can be used in any organism with an available reference, thus facilitating the discovery of protein-protein interactions in non-model organisms.

bioinformatics↗

bioRxiv2020

Y2H-SCORES: A statistical framework to infer protein-protein interactions from next-generation yeast-two-hybrid sequence data

Interactomes embody one of the most effective representations of cellular behavior by revealing function through protein associations. In order to build these models at the organism scale, high-throughput techniques are required to identify interacting pairs of proteins. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate protein-protein interaction networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we describe a statistical framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens that considers key aspects of experimental design, normalization, and controls. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS identified conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, accelerating the biological interpretation of experimental results. Proof-of-concept was demonstrated by discovery and validation of a novel interaction between the barley powdery mildew effector, AVRA13, with the vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1. Author SummaryOrganisms respond to their environment through networks of interacting proteins and other biomolecules. In order to investigate these interacting proteins, many in vitro and in vivo techniques have been used. Among these, yeast two-hybrid (Y2H) has been integrated with next generation sequencing (NGS) to approach protein-protein interactions on a genome-wide scale. The fusion of these two methods has been termed next-generation-interaction screening, abbreviated as Y2H-NGIS. However, the massive and diverse data sets resulting from this technology have presented unique challenges to analysis. To address these challenges, we optimized the computational and statistical evaluation of Y2H-NGIS to provide metrics to identify high-confidence interacting proteins under a variety of dataset scenarios. Our proposed framework can be extended to different yeast-based interaction settings, utilizing the general principles of enrichment, specificity, and in-frame prey selection to accurately assemble protein-protein interaction networks. Lastly, we showed how the pipeline works experimentally, by identifying and validating a novel interaction between the barley powdery mildew effector AVRA13 and the barley vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1. Y2H-SCORES software is available at GitHub repository https://github.com/Wiselab2/Y2H-SCORES.

systems biology↗

bioRxiv2020

Cholesterol in membranes facilitates aggregation of amyloid beta protein at physiologically low concentrations

The formation of amyloid {beta} (1-42) (A{beta}42) oligomers is considered to be a critical step in the development of Alzheimers disease (AD). However, the mechanism underlying this process at physiologically low concentrations of A{beta}42 remains unclear. We have previously shown that oligomers assemble at such low A{beta}42 monomer concentrations in vitro on phospholipid membranes. We hypothesized that membrane composition is the factor controlling the aggregation process. Accumulation of cholesterol in membranes is associated with AD development, suggesting that insertion of cholesterol into membranes may initiate the A{beta}42 aggregation, regardless of a low monomer concentration. We used atomic force microscopy (AFM) to directly visualize the aggregation process of A{beta}42 on the surface of a lipid bilayer containing cholesterol. Time-lapse AFM imaging unambiguously demonstrates that cholesterol in the lipid bilayer significantly enhances the aggregation process of A{beta}42 at nanomolar monomer concentration. Quantitative analysis of the AFM data shows that both the number of A{beta}42 oligomers and their sizes grow when cholesterol is present. Importantly, the aggregation process is dynamic, so the aggregates assembled on the membrane can dissociate from the bilayer surface into the bulk solution. Computational modeling demonstrated that the lipid bilayer containing cholesterol had an elevated affinity to A{beta}42. Moreover, monomers adopted the aggregation-prone conformations present in amyloid fibrils. The low energy barriers between these conformations facilitate the transition between monomer states and is another factor promoting the self-association of the monomers.

biophysics↗

bioRxiv2020

An Assistive Computer Vision Tool to Automatically Detect Changes in Fish Behavior In Response to Ambient Odor

The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fishs environment warrants a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.

animal behavior and cognition↗

bioRxiv2020

Development of a copper-graphene nanocomposite based transparent coating with antiviral activity against influenza virus

Respiratory infections by RNA viruses are one of the major burdens upon global health and economy. Viruses like influenza or coronaviruses can be transmitted through respiratory droplets or contaminated surfaces. An effective antiviral coating can decrease the viability of the virus particles in the outside environment significantly, hence reducing their transmission rate. In this work, we have screened a series of nanoparticles and their composites for antiviral activity using Nano Luciferase based highly sensitive influenza A reporter virus. Using this screening system, we have identified copper-graphene (Cu-Gr) nanocomposite shows strong antiviral activity. Extensive material and biological characterization of the nanocomposite suggested a unique metal oxide embedded graphene sheet architecture that can inactivate the virion particles only within 30 minutes of pre-incubation and subsequently interferes with the entry of these virion particles into the host cell. This ultimately results in reduced viral gene expression, replication and production of progeny virus particles, slowing down the overall pace of progression of infection. Using PVA as a capping agent, we have been able to generate a Cu-Gr nanocomposite based highly transparent coating that retains its original antiviral activity in the solid form.

microbiology↗

bioRxiv2020

Tissue pressure and cell traction compensate to drive robust aggregate spreading

In liquid droplets, the balance of interfacial energies and substrate elasticity determines the shape of the droplet and the dynamics of wetting. In living cells, interfacial energies are not constant, but adapt to the mechanics of their environment. As a result, the forces driving the dynamics of wetting for cells and tissues are unclear and may be context specific. In this work, using a combination of experimental measurements and modeling, we show the surface tension of cell aggregates, as models of active liquid droplets, depends upon the size of the aggregate and the magnitude of applied load, which alters the wetting dynamics. Upon wetting rigid substrates, traction stresses are elevated at the boundary, and tension drives forward motion. By contrast, upon wetting compliant substrates, traction forces are attenuated, yet wetting occurs at a comparable rate. In this case, capillary forces at the contact line are elevated and aggregate surface tension contributes to strong outward, pressure-driven cellular flows. Thus, cell aggregates adapt to the mechanics of their environments, using pressure and traction as compensatory mechanisms to drive robust wetting.

biophysics↗

bioRxiv2020

Non-coding RNAs in fluid shear stress-driven and reactive oxygen species-mediated colon cancer metastasis

BackgroundColon adenocarcinoma (COAD) is the third most common cancer in the world. Fluid shear stress (FSS) and intracellular reactive oxygen species (ROS) levels are known to mediate COAD metastasis. The present work was performed to explore the role of regulatory non-coding RNA molecules associated with FSS and ROS in COAD metastasis. MethodsThe interactions between the mRNAs associated with FSS and ROS, the corresponding miRNAs, lncRNAs and circRNAs in COAD metastasis were used to generate the mRNA-miRNA-lncRNA-circRNA network. The expression levels of the RNAs in the network were also considered besides the identification of RNA hubs and modules. Further, functional enrichment and survival analysis of the significant miRNAs together with the OncoPrint as well as survival analysis of the selected mRNAs were performed. Subsequently, their functional role was also corroborated with existing literature. ResultsTen significant miRNA hubs were identified, out of which hsa-miR-17-5p and hsa-miR-20a-5p were found to interact with a lncRNA, CCAT2 and hsa-miR-335 was found to interact with four circRNAs. 60% of the FSS and ROS associated mRNAs and 90% of the top 10 miRNA-enriched pathways that emerged from the functional analysis were reported to be involved in COAD metastasis. 15 significant miRNAs were identified in ten different modules suggesting their importance in FSS and ROS mediated COAD metastasis. Finally, ten miRNAs and three mRNAs associated with FSS and/or ROS were identified as significant overall survival markers; 33 mRNAs were also identified as metastasis-free survival markers whereas 15 mRNAs showed >10% gene alterations in TCGA-COAD data and hence emerged as significant molecular markers in the process. ConclusionWe hypothesize that the biologically significant RNAs identified in this integrated analysis may provide valuable insights to understand the molecular mechanism of the FSS driven and ROS mediated COAD metastasis and to design efficient treatment strategies.

systems biology↗

bioRxiv2020

Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2

The novel Corona Virus Disease 2019 (COVID-19) pandemic has set the fatality rates ablaze across the world. So, to combat this disease, we have designed a multi-epitope vaccine from various proteins of Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) with an immuno-informatics approach, validated in silico to be stable, non-allergic and antigenic. Cytotoxic T-cell, helper T-cell, and B-cell epitopes were computationally predicted from six conserved protein sequences among four viral strains isolated across the world. The T-cell epitopes, overlapping with the B-cell epitopes, were included in the vaccine construct to assure the humoral and cell-mediated immune response. The beta-subunit of cholera toxin was added as an adjuvant at the N-terminal of the construct to increase immunogenicity. Interferon-gamma inducing epitopes were even predicted in the vaccine. Molecular docking and binding energetics studies revealed strong interactions of the vaccine with immune-stimulatory toll-like receptors (TLR) -2, 3, 4. Molecular dynamics simulation of the vaccine ensured in vivo stability in the biological system. The immune simulation of vaccine evinced elevated immune response. The efficient translation of the vaccine in an expression vector was assured utilizing in silico cloning approach. Certainly, such a vaccine construct could reliably be effective against COVID-19.

bioinformatics↗

bioRxiv2020

Comparative analysis of clonal evolution among patients with right-sided colon cancer, left-sided colon cancer and rectal cancer

Background Tumor multi-region sequencing reveals intratumor heterogeneity (ITH) and clonal evolution which play a key role in progression and metastases of the tumor. However, large-scale high depths multiregional sequencing of colorectal cancer (CRC) has not been well studied. In addition, the comparative analysis among right-sided colon cancer (RCC), left-sided colon cancer (LCC) and rectal cancer (RC) patients as well as the study of lymph node metastasis (LN) with extranodal tumor deposits (ENTD) from evolutionary perspective remain unknown.Results In this prospective study, we recruited different stages of 68 CRC patients with RCC (18), LCC (20) and RC (30). We performed high-depth whole exome sequencing (WES) of 206 tumor regions including 176 primary tumors, 19 LN and 11 ENTD samples. Our results showed ITH with a Darwinian pattern of evolution. We identified that the evolution pattern of LCC and RC was more complex and divergent than RCC, suggesting the evolutionary diversity in the initiation and progression of LCC and RC. Genetic and evolutionary evidences found that both LN and ENTD were of polyclonal in origin. Moreover, ENTD was a distinct entity from LN and evolved later.Conclusions In conclusion, our study showed the Darwinian pattern of evolution with differences in clonal evolution between RCC with LCC and RC.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2020

Machine Learning Identifies Complicated Sepsis Trajectory and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

A complicated clinical course for critically ill patients admitted to the ICU usually includes multiorgan dysfunction and subsequent death. Owning to the heterogeneity, complexity, and unpredictability of the disease progression, patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 hours of PICU admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes that were predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with ten iterations, the overall mean area under the curve reached 0.82. Using the same set of genes, we further achieved an overall area under the curve of 0.72 when tested on an external validation set. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1 and NLRP1 that help to predict sepsis severity or mortality. The discovery of eight novel genetic biomarkers related to the overactive innate immune system and neutrophils functions, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSTranscriptomic biomarkers have long been explored as potential means of earlier disease endotyping. Much of the existing literature has however focused on mortality and discrete outcomes. Additionally, much of prior work in this area has been developed on statistical methods, while recent means of selecting features have not been sufficiently explored. Added value of this studyIn this study, we developed a robust machine learning based model for identifying novel biomarkers of complicated disease courses. We found 20 highly stable genes that predict disease complexity with an average derivation AUROC of 0.82 and validation AUROC of 0.72 within critically ill children, using peripheral blood collected within 24 hrs of ICU admission. Implications of all the available evidenceEarlier identification of disease complexity can inform care management and targeted therapy. Therefore, the 20 gene candidates identified by our rigorous approach, can be used to identify, early in their ICU stay, patients who may ultimately develop significant organ dysfunction and complex care management.

immunology↗