Biology subjects

Kashyap, A.

Publications and source records attributed to Kashyap, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Rapid Antimicrobial Resistance Decline Coupled with Microbial Community Shifts in Sewage Polluted River Mesocosms

Antimicrobial resistance poses a global health threat, yet the fate of antimicrobial resistance genes (ARGs) and microbial communities from wastewaters in receiving rivers is rarely quantified. This study investigated the degradation kinetics of ARGs in sewage-polluted river water mesocosms and under sulfamethoxazole and copper stress, separately and in combination. Seven clinically relevant ARGs (blaCTX-M, blaNDM, qnrS, sul2, tetW, ermF, and aph(3'')-Ib), a mobile genetic element marker (intI1) and bacterial marker genes (uidA, 16S rDNA) were quantified by qPCR. Bacterial community changes were monitored using 16S rDNA amplicon sequencing. All target genes decayed with approximately first-order kinetics in all conditions, with qnrS (half-life: 8.0 h) and blaCTX-M (8.4 h) declining most rapidly and intI1 (32.5 h) and sul2 (46.5 h) declining most slowly. Ordination showed that ARG composition shifted primarily over time rather than by antibiotic/metal treatment. Bacterial communities shifted from initially Campylobacterota dominated ([≥] 90%) to Pseudomonadota, Bacillota, and Actinomycetota dominated communities over 168 h. Quantitative microbiome profiling showed a 95% reduction in ASV richness. PICRUSt2 analysis suggested progressive enrichment of aerobic and several metabolic pathways, as the mesocosms transitioned from highly polluted anoxic conditions to oxygenated conditions. Shifts in microbial community composition were coupled with changes in the resistome as identified by Procrustes analyses (r = 0.841, p = 0.001) and other analyses. Together, results show that river self-purification can rapidly reduce ARG loads, at least in warmer climates. This study provides approximate kinetic parameters for mathematical models to predict the fate of ARGs in rivers.

microbiology↗

bioRxiv2026

Expression-dependent but strand-independent synonymous single-nucleotide polymorphism in the Escherichia coli chromosome

AbstractMutation in genomes is mainly attributed to replication, though transcription is also known to be mutagenic and is a more frequent event than replication in an organism. Recently, there have been reports regarding genome-wide transcription-induced mutagenesis. However, a distinct demonstration of specific mutation being replication-dependent and/or transcription-dependent in genomes is yet to be established. Here, we studied synonymous single-nucleotide polymorphisms (SNPs) in 2091 individual coding sequences (CDS) in the leading strand (LeS) and the lagging strand (LaS) of the Escherichia coli chromosome by comparing across 157 strains. The frequencies of complementary transitions (ti) and complementary transversions (tv) were compared in each CDS to assess parity violation in the context of strand location and gene expression. The C[->]T and G[->]A exhibited the maximum frequency as well as the most prominent strand inequality as these tis were influenced both by the strand location as well as by the gene expression. Interestingly, inequality between T[->]C and A[->]G was expression-dependent but strand-independent. This was a direct demonstration of strand inequality due to expression but not due to replication. A[->]T and G[->]T tvs were universally more frequent than their complementary T[->]A and C[->]A, respectively. The strand-independent but expression-dependent synonymous SNP inequality in CDS, supports the role of transcription-induced mutagenesis contributing to strand inequality in the E. coli chromosome. Significance StatementMutational patterns in bacterial genomes are shaped by both replication and transcription, but their relative roles remain unclear. By analyzing synonymous SNPs across 157 strains of E. coli, we identify strong strand-specific asymmetry consistent with replication-associated biases. A strong asymmetry between the strands regarding C[->]T and G[->]A ti is observed, as it is influenced by replication asymmetry and expression. We further show that gene expression significantly influences mutation types, with T[->]C ti being enriched in the highly expressed genes and A[->]G ti being enriched in the lowly expressed genes. Notably, A[->]T ti is strand independent but exhibits mild dependency on gene expression, revealing a previously unrecognized mutational pattern. These findings highlight how some SNPs are influenced by transcription but not replication

evolutionary biology↗

bioRxiv2026

A multi-omic atlas in the African turquoise killifish reveals increased glucocorticoid signaling as a hallmark of brain aging

Aging is the leading risk factor for cognitive impairment and neurodegeneration, yet molecular changes that unfold in the brain over time, and how they drive this vulnerability, remain unclear. The naturally short-lived African turquoise killifish (Nothobranchius furzeri) offers a powerful model to understand brain aging on an accelerated timescale and test the impact of potential interventions. Here, we present a multi-omic atlas of brain aging of female and male African turquoise killifish from 2 independent genetic strains of different captive lifespans, encompassing single-nuclei RNA-seq, single nuclei ATAC-seq, and bulk ATAC-seq to capture transcriptional and regulatory changes. Interestingly, our atlas indicates that aging leads to a significant expansion of microglia numbers, regardless of sex or strain, which we independently validate using in-situ hybridization. In addition, we identify robust and conserved gene regulation changes, that are consistent with activation of glucocorticoid signalling as a hallmark (and potential driver) of vertebrate brain aging. Furthermore, pharmacological inhibition of glucocorticoid receptor activity starting at middle-age led to significant rescue of key molecular and cellular aging phenotypes. Thus, our study provides a powerful resource and framework to leverage the African turquoise killifish and rapidly uncover actionable pathways driving brain aging.

neuroscience↗

bioRxiv2026

The Receptor Kinase MEE39/ATHE Mediates Cell Wall Integrity Surveillance During Root Vascular Pathogen Infection

Plant cell wall (CW) integrity signaling enables early detection of microbial invasion, yet the receptors involved and their spatial and temporal dynamics during infection remain largely unknown. We identify ATHENA (ATHE)/MEE39, a previously uncharacterized malectin-like leucin-rich repeat receptor kinase (Mal-LRR-RK) that contributes to defense against the root vascular pathogen Fusarium oxysporum (Fo), particularly in outer root layers where colonization begins. ATHE abundance, localization, and endocytic trafficking are rapidly remodeled during infection, and loss of ATHE compromises basal immunity and early pathogen-induced transcriptional reprogramming. ATHE responds to altered cellulose synthesis, cellulose-derived oligosaccharides, mechanically induced CW perturbations, and the fungal secreted peptide Fo-RALF. In most of these contexts, ATHE acts together with the LRR-RK MIK2, forming a pathogen-strengthened RK complex that fine-tunes root responses to Fo. This represents the first example of a receptor complex visualized subcellularly in vivo during a plant-microbe interaction. Although Brassicaceae-specific, heterologous expression of ATHE enhanced tomato resistance to Fo, highlighting its functional relevance across plant lineages and its potential use for crop engineering. Our work reveals a previously unrecognized strategy by which plants decode microbial threats through dynamic CW-integrity surveillance.

plant biology↗

bioRxiv2025

Concurrent maternal stress and THC exposure during pregnancy alters adolescent behavioral outcomes and corticolimbic molecular programs

Cannabis use during pregnancy is increasing, often to alleviate stress and anxiety, yet the long-term effect of prenatal cannabis exposure alone or in combination with psychosocial stress on offspring neurodevelopment or maternal behaviors remains unclear. Here, we developed a translational rodent model combining prenatal {Delta}-tetrahydrocannabinol (THC) exposure with chronic psychosocial stress using the maternal witness defeat stress (MWDS) paradigm. Pregnant C57BL/6 mice were exposed to MWDS from gestational day (GD) 3-12 and received daily subcutaneous THC (2 mg/kg) or vehicle until birth. All exposure groups showed impaired maternal behavior, with negative postnatal outcomes and caregiving, with additive effects observed in the combined exposure group. In adolescence, male and female offspring exhibited exposure-specific behavioral alterations. Prenatal stress and combined exposures led to increased anxiety-like behavior and reduced motivated behavior in both sexes, while THC alone primarily impacted female self-care and social behavior. Transcriptomic profiling of the prefrontal cortex (PFC) and nucleus accumbens (NAc) of adolescent offspring revealed sex- and region-specific gene expression changes across all exposure groups. Prenatal THC-, stress-, and combined exposures each altered distinct molecular pathways related to mitochondrial function, synaptic organization, and glial signaling. Comparative analysis with a perinatal fentanyl model revealed shared transcriptional substrates involved in synaptic signaling and circadian regulation. These findings indicate that THC and stress independently and additively impair maternal behaviors with lasting neurodevelopment signatures in offspring.

neuroscience↗

bioRxiv2025

Complete360: A Multi-Omics Liquid Biopsy Platform with Attomole Sensitivity and High Reproducibility

Advancements in liquid biopsy platforms can uncovered disease-associated molecular changes that may indicate disease. Despite recent progress in the detection of a number of analytes, the current liquid biopsy platforms struggle with specificity, sensitivity, and reproducibility, limiting their clinical utility and high-throughput applicability. Here, we describe an ultra-sensitive mass spectrometry-based liquid biopsy platform, Complete360, with an optimized library of detection parameters, meticulously refined assays to enable the simultaneous detection and quantification of a broad range of body-fluid proteins, metabolites, and lipids. The system addresses sample-specific and target-specific noises, as well as the micro-environments of proteomics during co-elution, achieving unprecedented levels of reproducibility, specificity, and sensitivity. This platform has facilitated the detection and quantification of numerous multi-omics signatures in human plasma samples, uncovering novel disease mechanisms never identified by previous approaches. Our study establishes a platform for rapid discovery and validation of molecular changes in disease panels, demonstrating its potential utilities in applications meeting clinical regulation guidelines.

systems biology↗

bioRxiv2024

Field monitoring and hydraulic modelling quantify untreated wastewater as dominant source of AMR in a small river running through a big city

Quantifying sources of antimicrobial resistance (AMR) in rivers receiving various waste streams is essential for targeting mitigation strategies yet rarely performed. This study combined field monitoring with hydraulic modelling and mass balance calculations to attribute sources of AMR in the Musi River running through Hyderabad, India, a city renowned for pharmaceutical manufacturing. We quantified antibiotic resistance genes (ARGs), resistant bacteria (ARBs), and physicochemical parameters in water and sediment samples in the dry and wet season. Absolute ARG and ARB abundances spiked in the city, declining again downstream. Changes were more gradual in the wet season. Pollution levels were significantly different between upstream, city and downstream stretches and seasons. Hydraulic modelling revealed that 60-80% or 20-40% of the river water in the city derived from untreated sewage during the dry or wet seasons, respectively. This established municipal waste, not pharmaceutical sources, as the dominant driver of AMR in the Musi. Linear discriminant analysis identified dissolved oxygen and total nitrogen as reliable proxies for distinguishing AMR-polluted from less-polluted sites. The Musi, with insufficient wastewater treatment and limited dilution of point-source loadings, is typical for many urban rivers in resource-limited countries highlighting the urgent need for improved wastewater management to reduce AMR exposures.

microbiology↗

bioRxiv2024

Learning from and improving upon high-throughputscreens for protein fitness with Generative AI - Application to BBB-crossing AAV design

Deep Science is enabling high-throughput experimentation (HTE) to design novel biological entities with desired properties. E.g., blood-brain-barrier (BBB) crossing adeno-associated virus (AAV) vectors, needed for systemic delivery of gene therapies to brain cells, have been identified through innovative directed evolution assays such as M-CRE-ATE and TRACER. But, even these high-throughput experiments are only able to explore a miniscule portion of the large design space of biological entities. In this paper, we introduce autograd based maximization of protein fitness (AutoMaxProFit) to learn from and improve upon protein designs generated with high-throughput screens. Using a transformer based generative AI network and protein language models, we improve upon the design of a variant previously discovered through HTE, to yield 2x better enrichment in brain endothelial cells, as estimated by molecular dynamics (MD) simulations. This shows that Deep Tech models can learn from the observations generated by Deep Science experiments and go on to find more optimal design candidates for application in Biopharma.

bioinformatics↗

bioRxiv2024

Drug resistance through ribosome splitting and rRNA disordering in mycobacteria

AbstractHflX is known to rescue stalled ribosomes and is implicated in antibiotic resistance in several bacteria. Here we present several high-resolution cryo-EM structures of mycobacterial HflX in complex with the ribosome and its 50S subunit, with and without antibiotics. These structures reveal a distinct mechanism for HflX- mediated ribosome splitting and antibiotic resistance in mycobacteria. In addition to dissociating ribosome into two subunits, mycobacterial HflX mediates persistent disordering of multiple 23S rRNA helices to generate an inactive pool of 50S subunits. Mycobacterial HflX also acts as an anti-association factor by binding to pre-dissociated 50S subunits. A mycobacteria-specific insertion in HflX reaches further into the peptidyl transferase center. The position of this insertion overlaps with ribosome-bound macrolides or lincosamide class of antibiotics. The extended conformation of insertion seen in the absence of these antibiotics retracts and adjusts around the bound antibiotics instead of physically displacing them. It therefore likely imparts antibiotic resistance by sequestration of the antibiotic- bound inactive 50S subunits.

biochemistry↗

bioRxiv2023

Using an ODE model to separate Rest and Task signals in fMRI

Cortical activity results from the interplay between network-connected regions that integrate information and stimulus-driven processes originating from sensory motor networks responding to specific tasks. Separating the information due to each of these components has been challenging, and the relationship as measured by fMRI in each of these cases Rest (network) and Task (stimulus-driven) remains a significant open question in the study of large-scale brain dynamics. In this study, we develop a network ordinary differential equation (ODE) model using advanced system identification tools to analyze fMRI data from both rest and task conditions. We demonstrate that task-specific ODEs are essentially a subset of rest-specific ODEs across four different tasks from the Human Connectome Project. By assuming that task activity is a relative complement of rest activity, our model significantly improves predictions of reaction times on a trial-by-trial basis, leading to a 9 % increase in explanatory power (R2) across the 14 sub-tasks tested. We have additionally shown that these results hold for predicting missing trials and accuracy on a per individual basis as well as classifying Tasks trajectories or resulting dynamic Task functional connectivity. Our findings establish the principle of the Active Cortex Model, which posits that the cortex is always active and that Rest State encompasses all processes, while certain subsets of processes get elevated to perform specific task computations. Thus, this study is an important milestone in the development of the fMRI equation - to causally link large-scale brain activity, brain structural connectivity, and behavioral variables within a single framework.

neuroscience↗

bioRxiv2023

Disrupted endosomal trafficking of the Vangl-Celsr polarity complex causes congenital anomalies in trachea-esophageal separation

Disruptions in foregut morphogenesis can result in life-threatening conditions where the trachea and esophagus fail to separate properly, such as esophageal atresia (EA) and tracheoesophageal fistulas (TEF). The developmental basis of these congenital anomalies is poorly understood, but recent genome sequencing reveals that de novo variants in intracellular trafficking genes are enriched in EA/TEF patients. Here, we confirm that mutation of orthologous genes in Xenopus disrupts trachea-esophageal separation similar to EA/TEF patients. We show that the Rab11a recycling endosome pathway is required to localize Vangl-Celsr polarity complexes at the luminal cell surface where opposite sides of the foregut tube fuse. Partial loss of endosome trafficking or Vangl-Celsr complexes disrupts epithelial polarity and mutant cells accumulate at the fusion point, fail to downregulate Cadherin, and do not separate into distinct trachea and esophagus. These data provide insights into the mechanisms of congenital anomalies and general paradigms of tissue fusion during organogenesis.

developmental biology↗

bioRxiv2023

Lesion aware automated processing pipeline for multimodal neuroimaging stroke data and The Virtual Brain (TVB)

BackgroundProcessing stroke magnetic resonance imaging (MRI) brain data can be susceptible to lesion-based abnormalities. In this study we developed and validated the Lesion Aware automated Processing Pipeline (LeAPP) that incorporates mitigation measures, improving volumetric and connectomics outputs compared to current standards in automated MRI processing pipelines. MethodsBuilding upon the Human Connectome Project (HCP) minimal processing pipeline, we introduced correction measures, such as cost-function masking and virtual brain transplant, and extended functional and diffusion processing to match acquisition protocols often found in a clinical context. A total of 51 participants (36 stroke patients (65.7{+/-}12.96 years, 18 female) and 15 healthy controls (69.2{+/-}7.4 years, 7 female)) were processed across four time points for patients (3-5, 30-40, 85-95, 340-380 days after stroke onset) and one time point for controls. Artificially lesioned brains (N=82), derived from healthy brains and informed by real stroke lesions were created, thus generating ground-truth data for validation. The processing pipeline and validation framework are available as containerized open-source software. Reconstruction quality has been quantified on whole brain level and for lesion affected and unaffected regions-of-interest (ROIs) using metrics like dice score, volume difference and center-of-gravity distance. Global and local level connectome reconstruction was assessed using node strength, node centrality and clustering coefficient. ResultsThe new pipeline LeAPP provides close reconstructions of the ground truth. Deviations in reconstructed averaged whole brain node strength and all ROI based volume and connectome metrics were significantly reduced compared to the HCP pipeline without stroke specific mitigation measures. ConclusionsLeAPP improves reconstruction quality of multimodal MRI processing for brain parcellation and structural connectome estimation significantly over the non-adapted HCP in the presence of lesions and provides a robust framework for diffusion and functional image processing of clinical stroke data. This novel open-source automated processing pipeline contributes to a development towards reproducible research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/555078v1_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@ca58aborg.highwire.dtl.DTLVardef@8342f4org.highwire.dtl.DTLVardef@1a0d4b9org.highwire.dtl.DTLVardef@1edf1cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

The WRAD complex represents a therapeutically exploitable target for cancer stem cells in glioblastoma

Glioblastomas (GBMs) are heterogeneous, treatment-resistant tumors that are driven by populations of cancer stem cells (CSCs). Despite their importance for tumor growth, few molecular mechanisms critical for CSC population maintenance have been exploited for therapeutic development. We developed a spatially resolved loss-of-function screen in GBM patient-derived organoids to identify essential epigenetic regulators in the SOX2-enriched, therapy resistant niche and identified WDR5 as indispensable for this population. WDR5 is a component of the WRAD complex, which promotes SET1-family-mediated Lys4 methylation of histone H3, associated with positive regulation of transcription. In GBM CSC models, WDR5 inhibitors blocked WRAD complex assembly and reduced H3K4 trimethylation and expression of genes involved in CSC-relevant oncogenic pathways. H3K4me3 peaks lost with WDR5 inhibitor treatment occurred disproportionally on POU transcription factor motifs, including the POU5F1(OCT4)::SOX2 motif. We incorporated a SOX2/OCT4 motif driven GFP reporter system into our CSC cell models and found that WDR5 inhibitor treatment diminished reporter activity. Further, WDR5 inhibitor treatment altered the stem cell state, disrupting CSC in vitro growth and self-renewal as well as in vivo tumor growth. These findings highlight the role of WDR5 and the WRAD complex in maintaining the CSC state and provide a rationale for therapeutic development of WDR5 inhibitors for GBM and other advanced cancers. SignificanceIn this study, we perform an epigenetic-focused functional genomics screen in glioblastoma organoids and identify WDR5 as an essential epigenetic regulator in the SOX2-enriched, therapy resistant cancer stem cell niche. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=135 HEIGHT=200 SRC="FIGDIR/small/461125v2_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@bc5c5eorg.highwire.dtl.DTLVardef@545a2borg.highwire.dtl.DTLVardef@1ebd3c9org.highwire.dtl.DTLVardef@19d5d30_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

1.Brain Network Models (BNMs) are a family of dynamical systems that simulate whole brain activity using neural mass models to represent local activity in different brain regions that influence each other via a global structural network. Research has been interested in using these network models to explain measured whole brain activity measured via resting state functional magnetic resonance imaging (rs-fMRI). Properties computed over longer periods of simulated and measured data such as average functional connectivity (FC), have shown to be comparable with similar properties estimated from measured rs-fMRI data. While this shows that these network models have similar properties over the dynamical landscape, it is unclear how well simulated trajectories compare with empirical trajectories on a timepoint-by-timepoint basis. Previous studies have shown that BNMs are able to produce relevant features at shorter timescales, but analysis of short-term trajectories or transient dynamics as defined by synchronized predictions from BNM made at the same timescale as the collected data has not yet been conducted. Relevant neural processes exist in the time frame of measurements and are often used in task fMRI studies to understand neural responses to behavioral cues. Therefore, it is important to investigate how much of these dynamics are captured by our current brain simulations. To test the nature of BNMs short term trajectories against observed data, we utilize a deep learning technique known as Neural ODE that based on an observed sequence of fMRI measurements, estimates the initial conditions such that the BNMs simulation is synchronized to produce the closest trajectory relative to the observed data. We test to see if the parameterization of a specific well studied BNM, the Firing Rate Model, calculated by maximizing its accuracy in reproducing observed short term trajectories matches with the parameterized model that produces the best average long-term metrics. Our results show that such an agreement between parameterization using long and short simulation analysis exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity. Therefore, we conclude that there is evidence that by solving for initial conditions, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

neuroscience↗

bioRxiv2021

Induced ligno-suberin vascular coating and tyramine-derived hydroxycinnamic acid amides restrict Ralstonia solanacearum colonization in resistant tomato roots

The soil borne pathogen Ralstonia solanacearum is the causing agent of bacterial wilt, a devastating disease affecting major agricultural crops. R. solanacearum enters plants through the roots and reaches the vasculature, causing rapid wilting. We recently showed that tomato varieties resistant to bacterial wilt restrict bacterial movement in the plant. In the present work we go a step forward by identifying the physico-chemical nature of the barriers induced in resistant tomato roots in response to R. solanacearum. We describe that resistant tomato specifically responds to infection by assembling de novo a structural barrier at the vasculature formed by a ligno-suberin coating and tyramine-derived hydroxycinnamic acid amides (HCAAs). On the contrary, susceptible tomato does not form these reinforcements in response to the pathogen but instead displays lignin structural changes compatible with its degradation. Further, we show that overexpressing genes of the ligno-suberin pathway in a commercial susceptible variety of tomato restricts R. solanacearum movement inside the plant and slows disease progression, enhancing resistance to the pathogen. We thus propose that the induced barrier in resistant plants does not only restrict the movement of the pathogen, but may also prevent cell wall degradation by the pathogen and confer anti-microbial properties.

plant biology↗

bioRxiv2021

Successful IL-12 cancer immunotherapy requires NK cell-derived CCL5 for anti-tumor DC-T cell crosstalk

T cell-directed cancer immunotherapy often fails to generate lasting tumor control. Harnessing additional effectors of the immune response against tumors may strengthen the clinical benefit of immunotherapies. Here, we demonstrate that therapeutic targeting of the IFN{gamma}-IL-12 pathway relies on the ability of a population of tissue-resident NK (trNK) cells to orchestrate an anti-tumor microenvironment. Particularly, utilizing an engineered adenoviral platform, we show that paracrine IL-12 enhances functional DC-CD8 T cell interactions to generate adaptive anti-tumor immunity. This effect depends on the abundance of trNK cells and specifically their capacity to produce the cDC1-chemoattractant CCL5. Failure to respond to IL-12 and other IFN{gamma}-inducing therapies such as immune checkpoint blockade in tumors with low trNK cell infiltration could be overcome by intra-tumoral delivery of CCL5. Our findings reveal a novel barrier for T cell-focused therapies and offer mechanistic insights into how T cell-NK cell-DC crosstalk can be enhanced to promote anti-tumor immunity and overcome resistance. SignificanceWe identified the lack of CCL5-producing, tissue-resident NK (trNK) cells as a barrier to T cell-focused therapies. While IL-12 induces anti-tumoral DC-T cell crosstalk in trNK cellrich tumors, resistance to IL-12 or anti-PD-1 in trNK cellpoor tumors can be overcome by the additional delivery of CCL5.

immunology↗

bioRxiv2021

Structure of a Plant-Specific Partitivirus Capsid Reveals a Novel Coat Protein Architecture with a Hypervariable Protrusion

Persistent plant viruses may be the most common viruses in wild plants. A growing body of evidence for mutualism between such viruses and their hosts, suggests that they play an important role in ecology and agriculture. Here we present the structure of a plant-specific partitivirus capsid at 2.9 [A] resolution by Cryo-EM. Structural features, including the T=1 arrangement of 60 coat protein dimers, are shared with fungal partitiviruses and the picobirnavirus lineage of dsRNA viruses. However, the topology of the capsid is markedly different with protrusions emanating from, and partly comprising, the binding interface of coat protein dimers. We show that a disordered region at the apex of the protrusion is not required for capsid assembly and represents a hypervariable site characteristic of the plant-specific partitiviruses. These results suggest a structural basis for the acquisition of additional functions by partitivirus coat proteins that enables mutualistic relationships with diverse plant hosts.

microbiology↗

bioRxiv2019

Brain Network Constraints and Recurrent Neural Networks reproduce unique Trajectories and State Transitions seen over the span of minutes in resting state fMRI

Large scale patterns of spontaneous whole brain activity seen in resting state functional Magnetic Resonance Imaging (rsfMRI), are in part believed to arise from neural populations interacting through the structural fiber network [18]. Generative models that simulate this network activity, called Brain Network Models (BNM), are able to reproduce global averaged properties of empirical rsfMRI activity such as functional connectivity (FC) [7, 27]. However, they perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole brain data [20]. At very short timescales between measurements, it is not known how much of the variance these BNM can explain because they are not currently synchronized with the measured rsfMRI. We demonstrate that by solving for the initial conditions of BNM from an observed data point using Recurrent Neural Networks (RNN) and integrating it to predict the next time step, the trained network can explain large amounts of variance for the 5 subsequent time points of unseen future trajectory. The RNN and BNM combined system essentially models the network component of rsfMRI, and where future activity is solely based on previous neural activity propagated through the structural network. Longer instantiations of this generative model simulated over the span of minutes can reproduce average FC and the 1/f power spectrum from 0.01 to 0.3 Hz seen in fMRI. Simulated data also contain interesting resting state dynamics, such as unique repeating trajectories, called QPPs [22] that are highly correlated to the empirical trajectory which spans over 20 seconds. Moreover, it exhibits complex states and transitions as seen using k-Means analysis on windowed FC matrices [1]. This suggests that by combining BNMs with RNN to accurately predict future resting state activity at short timescales, it is learning the manifold of the network dynamics, allowing it to simulate complex resting state trajectories at longer time scales. We believe that our technique will be useful in understanding the large-scale functional organization of the brain and how different BNMs recapitulate different aspects of the system dynamics.

bioengineering↗