Biology subjects

Khan, A. A.

Publications and source records attributed to Khan, A. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Full-assembly screening reveals mobile antibiotic-resistance cargo missed by chromosome-only genomes

BackgroundProbiotic bacteria occupy the same gut niches as enteric pathogens, prompting concern that probiotic strains might carry or contribute mobile antibiotic-resistance genes (ARGs). Genome-based screening is routinely used to assess this risk, but many screens use chromosome-level assemblies that may omit plasmid-borne, high-mobility cargo. We quantified this effect and compared the mobile context resistomes of probiotic-associated and pathogen reference genomes. MethodsWe screened 50 bacterial reference genomes (25 probiotic-associated, 25 pathogen/comparator) using a reproducible workflow with the CARD nucleotide catalog, PlasmidFinder replicons, and ISfinder insertion sequences. Each ARG was assigned a fourtier in silico mobile-context risk category from plasmid co-localization and insertion-sequence (IS) flanking. The identical strain panel was screened in matched full-assembly and chromosome-only modes. Acquired calls were curated against intrinsic/efflux/biocide determinants and cross-checked with AMRFinderPlus, ResFinder, and targeted BLAST, with MOB-suite as a plasmid/mobility overlay. ResultsFull-assembly screening detected 373 ARG loci versus 338 in chromosome-only mode on the same strains, increasing High-risk calls from 4 to 15 and recovering 32 plasmid replicons (chromosome-only: 0). All 15 High-risk mobile-context loci occurred in pathogen/comparator genomes and none in probiotic-associated genomes; no ARG was shared across groups at [≥]95% nucleotide identity (0/175 edges). Per-strain ARG burden was higher in pathogen genomes (mean 12.32 versus 0.52 loci; Mann-Whitney U = 606.5, P< 0.001). Most priority High-risk loci were corroborated by one or more external tools, with discordant calls retained explicitly as flagged records. ConclusionsChromosome-only screening materially undercounts mobile ARG cargo. In this reference-genome panel, high-risk mobile-context loci were concentrated in pathogen/comparator genomes -- an in silico reference-genome-level safety signal rather than evidence about commercial products or genetic transfer. Data SummaryNo new sequencing data were generated; all genomes are publicly available reference assemblies from NCBI RefSeq. O_LIGenome accessions for all 50 genomes are in Supplementary Table S1. C_LIO_LISource code (screening, validation, and figure scripts, including make_figures.py) is available at https://github.com/abdullahak07/1dprob. C_LIO_LIProcessed result tables are summarised in Supplementary Tables S2-S10. C_LIO_LIExternal validation outputs (AMRFinderPlus, ResFinder, BLAST, MOB-suite) are provided as supplementary data. C_LIO_LITool/database versions and detection thresholds are in Supplementary Table S9. The authors confirm that all supporting data, code, and protocols are available within the article, the cited repositories, or the supplementary material. C_LI O_TBL View this table: org.highwire.dtl.DTLVardef@b0f2beorg.highwire.dtl.DTLVardef@110c676org.highwire.dtl.DTLVardef@5578ceorg.highwire.dtl.DTLVardef@16e448forg.highwire.dtl.DTLVardef@573000_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOSupplementary Table S9:C_FLOATNO O_TABLECAPTIONtool/database versions and detection thresholds used in the SCALE50 analysis. Database snapshots and external validation outputs should be archived with the supplementary data to ensure reproducibility. C_TABLECAPTION C_TBL Impact StatementGenome-based screening is widely used to judge whether a bacterial strain carries transferable antibiotic-resistance genes, including in the safety assessment of probiotic-associated species. We show that the assembly level chosen for such screening materially affects the result: on an identical panel of 50 reference genomes, chromosome-only analysis recovered fewer than a third of the high-risk, mobile-context resistance loci detected when plasmid replicons were included. Applying full-assembly screening with transparent, multi-tool external validation, high-risk mobile-context resistance genes were concentrated in pathogen/comparator genomes and absent from the probiotic-associated genomes in this panel, with no cross-group sharing at high identity. These observations argue for full-assembly inputs and explicit mobile-context interpretation in genome-based resistance screening and provide a cautious, reference-genome-level safety signal; they are not claims about commercial products and do not demonstrate genetic transfer.

microbiology↗

bioRxiv2026

Genotype-Dependent Dysregulation of the MDM2-p53 Axis and Breast Cancer Susceptibility in Bangladeshi Women: A Cas-Control Study

BackgroundThe MDM2-p53 signaling pathway plays a central role in tumor suppression, and genetic variants that disrupt this pathway may influence breast cancer (BC) susceptibility. However, data from South Asian populations, particularly Bangladesh, remain limited. MethodsA case-control study was conducted in Bangladeshi women, including BC patients and healthy controls (HCs). Genotyping of MDM2 polymorphisms was performed using PCR-based methods. Circulating MDM2 and p53 protein levels were measured using enzyme-linked immunosorbent assays (ELISA). Associations between genotype, protein levels, BC status, and clinicopathological features were evaluated using appropriate statistical models. ResultsA strong and genotype-specific association was observed for MDM2 rs2279744. Women carrying the heterozygous TG genotype had a markedly increased risk of BC across additive, dominant, and over-dominant models, whereas the GG genotype showed a protective effect under the recessive model. In contrast, rs937282 did not show a significant association with BC risk. Circulating MDM2 levels were significantly elevated in patients compared with controls and varied by rs2279744 genotype, while circulating p53 levels showed an opposite trend. A strong inverse correlation was observed between serum MDM2 and p53 levels, supporting dysregulation of the MDM2-p53 feedback loop. Elevated MDM2 levels were also noted in HER2-positive and triple-positive BC subtypes. ConclusionTogether, these findings indicate that the MDM2 rs2279744 polymorphism contributes to BC susceptibility in a genotype-specific manner, likely through disruption of the MDM2-p53 regulatory balance. However, the absence of functional validation limits direct causal inference.

cancer biology↗

bioRxiv2026

Integron-Mediated Convergence of Carbapenemase and Disinfectant Resistance in Acinetobacter spp. from Critical Care Units

Acinetobacter spp. represents critical opportunistic pathogens driving severe bloodstream infections (BSIs) in intensive care unit (ICU) and neonatal intensive care unit (NICU) settings. The convergence of carbapenem resistance and emerging biocide tolerance, often mediated by mobile genetic elements, has intensified concerns regarding co-selection and persistence in clinical environments. A total of 90 molecularly confirmed Acinetobacter isolates (ICU = 44; NICU = 46) from bloodstream infections were analyzed. Antimicrobial susceptibility was determined using the Kirby-Bauer disk diffusion method in accordance with CLSI M100 (2024) guidelines and extended-spectrum {beta}-lactamase production was assessed by combined disc diffusion. Polymerase chain reaction (PCR) was employed to detect carbapenemase genes (blaVIM, blaNDM, blaIMP, blaOXA-23, blaOXA-58), biocide resistance determinants (qacE, qac{Delta}E1), and the class 1 integron-integrase gene (intI1). Multidrug-resistant (MDR) and extensively drug-resistant (XDR) phenotypes were identified in 71.1% (64/90) and 22.2% (20/90) of isolates, respectively. High resistance (>71%) was observed against meropenem and cephalosporins, whereas colistin (58.8%, 53/90) and amikacin (47.8%, 43/90) showed moderate susceptibility. The most prevalent genotypes were qac{Delta}E1 (76.6%, 69/90) and blaVIM (56.6%, 51/90). Statistical and network analyses revealed significant correlations between biocide and carbapenemase genes, identifying IntI1 as a primary driver of co-resistance. The findings indicate a significant co-occurrence of carbapenemase genes, biocide resistance determinants, and class 1 integrons among Acinobacter spp. isolates. These associations suggest that mobile genetic elements may contribute to the dissemination of resistance traits.

microbiology↗

bioRxiv2026

AI-Guided CRISPR Screen Accelerates Discovery of New Drug Targets

Psoriasis affects over 125 million people worldwide, yet the mechanistic understanding of keratinocyte-driven inflammation remains incomplete, limiting therapeutic innovation beyond costly systemic biologics that are prone to side effects. Here, we performed the first genome-wide CRISPR knockout screen in primary human adult epidermal keratinocytes to systematically identify regulators of IL-17 receptor A (IL17RA), a central node in psoriatic inflammation. To prioritize therapeutically tractable targets from over 19,000 screened genes, we integrated a large language model - VirtualCRISPR - trained on functional genomics data, identifying arachidonate 5-lipoxygenase (ALOX5) and oxytocin receptor (OXTR) as high-confidence novel hits with minimal prior association with psoriasis. Multi-omics validation revealed that ALOX5 and OXTR regulate IL17RA expression through distinct signaling pathways - ALOX5 through lipid mediators that stabilize the receptor at the cell surface, and OXTR through calcium signaling that reprograms cellular metabolism. Topical delivery of their inhibitors Zileuton (ALOX5) and Cligosiban (OXTR) exhibited therapeutic efficacy comparable to systemic anti-IL17RA antibody in the imiquimod-induced psoriasis model, suppressing pathogenic Th17/Tc17 responses, polarizing macrophages toward anti-inflammatory phenotypes, and normalizing epidermal hyperproliferation. Proteomic profiling in human 3D organotypic skin and murine models confirmed on-target pharmacology and revealed convergent suppression of neutrophil-keratinocyte inflammatory circuits. The use of VirtualCRISPR significantly shortened the timescale from screen to the identification of druggable hits with robust validation, and this work establishes a blueprint for integrating AI-driven target prioritization with functional genomics to accelerate therapeutic discovery.

genomics↗

bioRxiv2026

Virtual Cells Need Context, Not Just Scale

The intersection of AI and biology has entered a phase of explosive growth, driven by the ambition to build "Virtual Cells" or computational models capable of predicting cellular responses to any perturbation. Following the success of structural biology (e.g., AlphaFold) and large language models, the field has converged on training massive, high-capacity models on large-scale single-cell data. This position paper argues that scaling model capacity is insufficient to solve the Virtual Cell problem because the primary failure mode is a lack of adequate coverage over diverse biological contexts, not insufficient model expressivity. We support this claim by reviewing recent studies showing that simple baselines perform on par with sophisticated architectures within a given biological context, and current models fail to consistently generalize across contexts. We connect this finding to the causal inference literature on transportability and contrast it with domains where scaling has succeeded. We substantiate our argument through analysis of a state-of-the-art model on a 22-million-cell immunology dataset. We conclude that the community faces a causal transport problem that cannot be solved by accumulating more data from the same distributions. Instead, we argue that contextual diversity and causal representation learning deserve increased emphasis, complementing ongoing scaling of model capacity and data volume.

genomics↗

bioRxiv2026

DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions

Modeling recognition between T-cell receptors (TCRs) and peptide-MHC (pMHC) complexes is a fundamental challenge in computational immunology, constrained by sparse paired interaction data relative to abundant unpaired sequences. We introduce DecoderTCR, a masked language model framework that addresses this through two contributions: (1) a compositional continual pre-training curriculum that learns component representations from marginal data before refining cross-chain dependencies from limited pairs, and (2) Iterative Entropy-Guided Refinement (IEGR), a non-autoregressive decoding algorithm that resolves high-confidence positions first to provide context for uncertain regions. On held-out benchmarks, DecoderTCR achieves 0.96 AUROC for zero-shot pMHC binding prediction and 0.76 AUROC for epitope-specific TCR recognition, approaching supervised baselines without epitope-specific training. Learned representations recover structural contacts without coordinate supervision, and generated sequences exhibit realistic recombination statistics. Experimental validation reveals a prediction-generation gap: strong discrimination does not yet yield reliable generation, highlighting an open challenge for the field.

bioinformatics↗

bioRxiv2025

PRSformer: Disease Prediction from Million-Scale Individual Genotypes

Predicting disease risk from DNA presents an unprecedented emerging challenge as biobanks approach population scale sizes (N > 106 individuals) with ultra-high-dimensional features (L > 105 genotypes). Current methods, often linear and reliant on summary statistics, fail to capture complex genetic interactions and discard valuable individual-level information. We introduce PRSformer, a scalable deep learning architecture designed for end-to-end, multitask disease prediction directly from million-scale individual genotypes. PRSformer employs neighborhood attention, achieving linear O(L) complexity per layer, making Transformers tractable for genome-scale inputs. Crucially, PRSformer utilizes a stacking of these efficient attention layers, progressively increasing the effective receptive field to model local dependencies (e.g., within linkage disequilibrium blocks) before integrating information across wider genomic regions. This design, tailored for genomics, allows PRSformer to learn complex, potentially non-linear and long-range interactions directly from raw genotypes. We demonstrate PRSformers effectiveness using a unique large private cohort (N {approx} 5M) for predicting 18 autoimmune and inflammatory conditions using L {approx} 140k variants. PRSformer significantly outperforms highly optimized linear models trained on the same individual-level data and state-of-the-art summary-statistic-based methods (LDPred2) derived from the same cohort, quantifying the benefits of non-linear modeling and multitask learning at scale. Furthermore, experiments reveal that the advantage of non-linearity emerges primarily at large sample sizes (N > 1M), and that a multi-ancestry trained model improves generalization, establishing PRSformer as a new framework for deep learning in population-scale genomics.

genetics↗

bioRxiv2025

EHE cell cultures: a platform for mechanistic and therapeutic investigation

Epithelioid hemangioendothelioma (EHE) is a difficult to treat vascular sarcoma defined by TAZ- CAMTA1 or YAP-TFE3 fusion proteins. Human cell lines needed to further understand the pathogenesis of EHE have been lacking. Herein, we describe a method to generate EHE extended primary cell cultures. An integrated multi -omic and functional approach was used to characterize these cultures. The cell cultures, relatively homogenous by single cell RNA-Seq, demonstrated established characteristics of EHE including increased proliferation, anchorage independent growth, as well as the overall gene expression profile and secondary genetic alterations seen in EHE. Whole genome sequencing (WGS) identified links to epigenetic modifying complexes, metabolic processes, and pointed to the importance of the extracellular matrix (ECM) in these tumors. Bulk RNA-Seq demonstrated upregulation of pathways including PI3K-Akt signaling, ECM/ECM receptor interaction, and the Hippo signaling pathway. Development of these extended primary cell cultures allowed for single-cell profiling which demonstrated different cell compartments within the cultures. Furthermore, the cultures served as a therapeutic platform to test the efficacy of TEAD inhibitors in vitro. Overall, the development of EHE primary cell cultures will aid in the mechanistic understanding of this sarcoma and serve as a model system to test new therapeutic approaches.

cancer biology↗

bioRxiv2025

PI3K regulates TAZ/YAP and mTORC1 axes that can be synergistically targeted

Abstract/SummaryO_ST_ABSPurposeC_ST_ABSSarcomas are a heterogeneous group of cancers with few shared therapeutic targets. PI3K signaling is activated in various subsets of sarcomas, representing a shared oncogenic signaling pathway. Oncogenic PI3K signaling has been challenging to target therapeutically. An integrated view of PI3K and Hippo pathway signaling is examined to determine if this could be leveraged therapeutically. Experimental designA tissue microarray containing sarcomas of various histological types was evaluated for PTEN loss and correlated with levels of activated TAZ and YAP. PI3K and Hippo pathways were dissected in sarcoma cell lines. The role of TAZ and YAP were evaluated in a PI3K-driven mouse model. The efficacy of mTORC1 inhibition and TEAD inhibition were evaluated in sarcoma cell lines and in vivo. ResultsPI3K signaling is frequently activated in sarcomas due to PTEN loss (in 30-60%), representing a common therapeutic target. TAZ and YAP are transcriptional co-activators regulated by PI3K and drive a transcriptome necessary for tumor growth in a PI3K-driven sarcoma mouse model. Combination therapy using IK-930 (TEAD inhibitor) and everolimus (mTORC1 inhibitor) synergistically diminished proliferation and anchorage independent growth of PI3K-activated sarcoma cell lines at low, physiologically achievable doses. Furthermore, this combination therapy showed a synergistic effect in vivo, reducing tumor proliferation and size. ConclusionsTAZ and YAP are transcriptional co-activators downstream of PI3K signaling, a pathway that has lacked a well-defined oncogenic transcription factor. This PI3K-TAZ/YAP axis exists in parallel to the known PI3K-Akt-mTORC1 axis allowing for synergistic combination therapy targeting the TAZ/YAP-TEAD interaction and mTORC1 in sarcomas.

cancer biology↗

bioRxiv2024

The CoREST-complex regulates MYC stability and promotes post-transcriptional mRNA splicing in melanoma

The CoREST complex is a multi-subunit epigenetic regulator implicated in histone modification and transcriptional repression, but its role in tumorigenesis is not well-defined. Here, we show that the CoREST complex directly interacts with and stabilizes the MYC oncoprotein in cancer cells through site-specific deacetylation of lysine residues, primarily mediated by HDAC1/2. These modifications protect MYC from proteasomal degradation independently of transcriptional regulation, maintaining high MYC protein levels in cancer cells. Transcriptomic analysis reveals that the CoREST-mediated MYC stabilization activates transcription of genes critical for DNA replication and mitotic chromosome segregation, and enhances melanoma cell viability. These findings suggest that the CoREST complex maintains cancer cell genome stability and promotes survival by sustaining MYC oncogenic activity, highlighting it as a potential therapeutic target in MYC-driven malignancies.

cancer biology↗

bioRxiv2024

U1 snRNP-Specific U1C Acts as the Gatekeeper of the Survival of Motor Neurons (SMN) Complex in snRNP Biogenesis

The stability and abundance of spliceosomal snRNPs are determined by the assembly of an Sm protein ring (Sm core) on each snRNA, a process facilitated by the survival of motor neuron (SMN) complex. Whereas the SMN complexs role as a chaperone is well-established, the mechanisms that regulate its activity remain unclear. Our study identifies U1C, a U1 snRNP-specific protein, as a key regulator of the SMN complex. U1C together with U1-70K facilitates U1 snRNP formation, rendering the SMN complex accessible for other snRNAs to assemble Sm cores. Remarkably, a prevalent cancer-associated mutation in U1 snRNA near the U1C binding site not only disrupts Sm core assembly but also sequesters the SMN complex, inhibiting canonical snRNP formation. Our findings provide mechanistic insights into how snRNP-specific proteins regulate the SMN complex and suggest that U1 snRNA mutations in numerous cancers disrupt the SMN complex, leading to perturbation in various RNA metabolism pathways. HighlightsO_LIU1C facilitates the formation of Sm core not only on U1 snRNA but also on other snRNAs. C_LIO_LIU1C together with U1-70K promotes U1 snRNP formation, enabling the SMN complex to recruit Gemin5/other snRNAs complex. C_LIO_LIThe divalent binding of U1C to the SMN complex is mediated by U1Cs N-terminus helix, interacting with U1-70K, and its arginine-methylated C-terminus domain, binding to the SMN protein. C_LIO_LIMultiple U1 snRNA mutations in cancer patients defect in Sm core assembly and potentially sequester the SMN complex, thereby inhibiting canonical snRNP formation. C_LI

biochemistry↗

bioRxiv2024

A panel of phenotypically and genotypically diverse bioluminescent: fluorescent Trypanosoma cruzi strains as a resource for Chagas disease research

Chagas disease is caused by Trypanosoma cruzi, a protozoan parasite that displays considerable genetic diversity. Infections result in a range of pathological outcomes, and different strains can exhibit a wide spectrum of anti-parasitic drug tolerance. The genetic determinants of infectivity, virulence and therapeutic susceptibility remain largely unknown. As experimental tools to address these issues, we have generated a panel of bioluminescent:fluorescent parasite strains that cover the diversity of the T. cruzi species. These reporters allow spatio-temporal infection dynamics in murine models to be monitored in a non-invasive manner by in vivo imaging, provide a capability to detect rare infection foci at single-cell resolution, and represent a valuable resource for investigating virulence and host:parasite interactions at a mechanistic level. Importantly, these parasite reporter strains can also contribute to the Chagas disease drug screening cascade by ensuring that candidate compounds have pan-species in vivo activity prior to being advanced into clinical testing. The parasite strains described in this paper are available on request. AUTHOR SUMMARYChagas disease results from infection with the protozoan parasite Trypanosoma cruzi and is a major public health problem throughout Latin America. T. cruzi is a genetically diverse species and infection can result in a wide range of pathological outcomes, mainly associated with the heart and/or digestive tract. Research on Chagas disease, ranging from fundamental biology to drug development, has been greatly aided by the availability of genetically modified parasite reporter strains that express bioluminescent:fluorescent fusion proteins. In combination with mouse models and imaging technology, these strains allow infections to be monitored in real-time, with high sensitivity, and infection foci to be visualised at single-cell resolution. Here, we describe an extensive panel of bioluminescent and fluorescent strains that cover the diversity of the T. cruzi species. These reporter strains, that are available on request, should have wide utility in many areas of Chagas disease research. In particular, as part of the drug development screening programme, they can be used to ensure that candidate compounds have in vivo activity across the species prior to being advanced into clinical testing.

microbiology↗

bioRxiv2023

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies. AvailabilityATAT is available at: https://github.com/StevenSong/tissue-alignment

bioinformatics↗

bioRxiv2023

TRIBAL: Tree Inference of B cell Clonal Lineages

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. AvailabilityTRIBAL is available at https://github.com/elkebir-group/tribal

immunology↗

bioRxiv2023

Dynamics of Trypanosoma cruzi infection in hamsters and novel association with progressive motor dysfunction

Chagas disease is a zoonosis caused by the protozoan parasite Trypanosoma cruzi. Clinical outcomes range from long-term asymptomatic carriage to cardiac, digestive, neurological and composite presentations that can be fatal in both acute and chronic stages of the disease. Studies of T. cruzi in animal models, principally mice, have informed our understanding of the biological basis of this variability and its relationship to infection and host response dynamics. Hamsters have higher translational value for many human infectious diseases, but they have not been well developed as models of Chagas disease. We transposed a real-time bioluminescence imaging system for T. cruzi infection from mice into female Syrian hamsters (Mesocricetus auratus). This enabled us to study chronic tissue pathology in the context of spatiotemporal infection dynamics. Acute infections were widely disseminated, whereas chronic infections were almost entirely restricted to the skin and subcutaneous adipose tissue. Neither cardiac nor digestive tract disease were reproducible features of the model. Skeletal muscle had only sporadic parasitism in the chronic phase, but nevertheless displayed significant inflammation and fibrosis, features also seen in mouse models. Whereas mice had normal locomotion, all chronically infected hamsters developed hindlimb muscle hypertonia and a gait dysfunction resembling spastic diplegia. With further development, this model may therefore prove valuable in studies of peripheral nervous system involvement in Chagas disease.

microbiology↗

bioRxiv2023

Comparative Analysis, Diversification and Functional Validation of Plant Nucleotide-Binding Site Domain Genes

Nucleotide-binding site (NBS) domain genes are one of a superfamily of resistance genes involved in plant responses to pathogens. The current study identified presumably identified 12,820 NBS-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes classified into 168 classes with several novel domain architectures patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBS etc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG0, OG1, OG2 etc.) and unique (highly specific to species; OG80, OG82 etc) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG2, OG6, and OG15 in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to CLCuD. The genetic variation between susceptible (Coker 312) and tolerant (Mac7) G. hirsutum accessions identified several unique variants in NBS genes of Mac7 (6,583 varaints) and Coker312 (5,173 variants). The protein-ligand and proteins-protein interaction showed a strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of cotton leaf curl disease virus. The silencing of GaNBS (OG2) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful to understand the plant adaptation mechanism.

plant biology↗

bioRxiv2023

Computational prediction of protein interactions on single cells by proximity sequencing

Proximity sequencing (Prox-seq) measures gene expression, protein expression, and protein complexes at the single cell level, using information from dual-antibody binding events and a single cell sequencing readout. Prox-seq provides multi-dimensional phenotyping of single cells and was recently used to track the formation of receptor complexes during inflammatory signaling in macrophages and to discover a new interaction between CD9/CD8 proteins on naive T cells. The distribution of protein abundance affects identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model for protein dimer formation on single cells and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq single-cell data, which resulted in more accurate and robust quantification of protein complexes. Finally, our model offers a simple way to investigate the behavior of Prox-seq under various biological conditions and guide users toward selecting the best analysis method for their data.

systems biology↗

bioRxiv2023

Behavioral Paradigm for the Evaluation of Stimulation-Evoked Somatosensory Perception Thresholds in Rats

Intracortical microstimulation (ICMS) of the somatosensory cortex via penetrating microelectrode arrays (MEAs) can evoke cutaneous and proprioceptive sensations for restoration of perception in individuals with spinal cord injuries. However, ICMS current amplitudes needed to evoke these sensory percepts tend to change over time following implantation. Animal models have been used to investigate the mechanisms by which these changes occur and aid in the development of new engineering strategies to mitigate such changes. Non-human primates are commonly the animal of choice for investigating ICMS, but ethical concerns exist regarding their use. Rodents are a preferred animal model due to their availability, affordability, and ease of handling, but there are limited choices of behavioral tasks for investigating ICMS. In this study, we investigated the application of an innovative behavioral go/no-go paradigm capable of estimating ICMS-evoked sensory perception thresholds in freely moving rats. We divided animals into two groups, one receiving ICMS and a control group receiving auditory tones. Then, we trained the animals to nose-poke - a well-established behavioral task for rats - following either a suprathreshold ICMS current-controlled pulse train or frequency-controlled auditory tone. Animals received a sugar pellet reward when nose-poking correctly. When nose-poking incorrectly, animals received a mild air puff. After animals became proficient in this task, as defined by accuracy, precision, and other performance metrics, they continued to the next phase for perception threshold detection, where we varied the ICMS amplitude using a modified staircase method. Finally, we used non-linear regression to estimate perception thresholds. Results indicated that our behavioral protocol could estimate ICMS perception thresholds based on [~]95% accuracy of rat nose-poke responses to the conditioned stimulus. This behavioral paradigm provides a robust methodology for evaluating stimulation-evoked somatosensory percepts in rats comparable to the evaluation of auditory percepts. In future studies, this validated methodology can be used to study the performance of novel MEA device technologies on ICMS-evoked perception threshold stability using freely moving rats or to investigate information processing principles in neural circuits related to sensory perception discrimination.

neuroscience↗