Biology subjects

Ali, S.

Publications and source records attributed to Ali, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

AI-Guided Design and AKT3 Degradation Synergize to Enhance Bispecific and Trispecific CAR-T Cell Persistence and Overcome Antigen Escape

The structural design of chimeric antigen receptors (CARs) is critical for achieving robust and durable anti-tumor responses, particularly when targeting multiple antigens to prevent tumor antigen escape. However, increasing CAR complexity can introduce structural vulnerabilities, leading to antigen-independent T cell activation, activation-induced cell death, and reduced CAR-T cell persistence. To overcome these challenges, we designed 10,824 CAR molecules across diverse formats and screened 1,452 constructs in-vitro to develop an artificial intelligence model, termed CAR-Mediated Self-Destruction (CARMSeD), which predicts CAR designs susceptible to self-activation and dysfunction. Guided by CARMSeD and structural CAR-CAR interaction modeling, we identified optimized CAR architectures incorporating ICOS and 4-1BB co-stimulatory domains. Humanized bispecific CARs targeting CD20/CD19 and CD22/CD19 demonstrated superior anti-tumor efficacy and persistence both in-vitro and in various xenograft mice models. To further extend CAR-T cell persistence, we engineered bispecific CARs integrated with an AKT3-targeted PROTAC strategy. Targeted degradation of AKT3 enhanced anti-tumor potency, promoted memory T cell formation, and enabled sustained responses even under tumor rechallenge and CD19 antigen-loss conditions. Mechanistically, these effects were mediated by metabolic reprogramming involving FOXO4; notably, FOXO4-deficient CAR-T cells exhibited impaired long-term persistence. Leveraging these mechanistic insights, we developed a trispecific CAR-T cell platform incorporating a bispecific T cell engager (BiTE) targeting CD22/CD3, combined with AKT3 PROTACs. These trispecific CAR-T cells achieved potent tumor eradication, even against malignancies lacking both CD19 and CD20 expression. Collectively, this study presents a comprehensive strategy combining structure-based design, AI-guided screening, and targeted protein degradation to engineer next-generation bi and trispecific CAR-T cells with enhanced persistence, broad antigen coverage, and superior therapeutic durability.

cancer biology↗

bioRxiv2025

A Plasmodium lipase is critical for the disruption of the parasitophorous vacuole membrane and egress of hepatic merozoites.

Malaria parasites develop within a specialized vacuole delimited by the parasitophorous vacuolar membrane (PVM). The exit of Plasmodium from host cells is a well-regulated process involving several molecules that disrupt the PVM to continue the life cycle. While several protein classes play a role in Plasmodium PVM rupture and merozoite egress, the first class of enzymes one would consider to act on membranes is lipases. However, the role of a lipase in PVM disruption remains unknown. Here, we characterized the role of UIS28 (upregulated in infective sporozoite gene 28) in Plasmodium berghei. Bioinformatic studies indicated that UIS28 is a class 3 lipase containing a fungal lipase-like domain. We found that UIS28 localizes to the PVM in infected hepatocytes. To understand the role of UIS28 in PVM disruption, we validated its lipase activity and found that it breaks down lipids into glycerol. Parasites lacking UIS28 develop normally in the blood and mosquito stages and mature fully into hepatic merozoites but show a defect in egress from host hepatocytes. We quantified the PVM rupture and reported that mutant parasites exhibit delayed egress due to impaired PVM disruption. Together, our results demonstrate that UIS28 is a lipase involved in the disruption of the PVM and the successful egress of hepatic merozoites. Author SummaryAn essential step in the life cycle of malaria parasites is their egress from hepatocytes, allowing them to transition from the liver stage to the blood stage. Liver stage parasites reside in a PV, which is surrounded by a PVM. Parasites must breakdown the PVM before they can infect other cells. This complex, multistep process is orchestrated by the parasite and involves several molecules to disrupt the PVM. However, little is known about these molecules. Here, we validated that UIS28 is indeed a lipase. We show that UIS28 is expressed in sporozoites and in the liver stage, and that it localizes to the PVM of parasites within hepatocytes. Disruption of UIS28 gene does not affect blood and mosquito stage development. However, mutant parasites show a defect in the disruption of the PVM and egress of hepatic merozoites. For the first time, we demonstrate the role of a lipase in PVM rupture during the release of liver-stage merozoites.

microbiology↗

bioRxiv2025

Leukemia Inhibitory Factor as a late-stage treatment for delayed white matter loss in concussive head injury

Leukemia Inhibitory Factor (LIF) is an injury-induced cytokine that peaks 48 hours after a traumatic brain injury (TBI). Juvenile LIF haplodeficient mice exhibit desynchronized glial responses, increased neurodegeneration, decreased axonal conductivity and behavioral deficits after a concussive head injury. Given the necessity of LIF during the acute recovery phase after injury, we hypothesized that intranasal LIF (IN-LIF) treatment would prevent neurodegeneration when administered during the chronic recovery period from a mild TBI (mTBI). Young adult male CD1 mice were subjected to a midline, closed-head frontal cortex injury using a flat metal impactor with a 3mm tip to induce a mTBI. In the 6-8 weeks post-mTBI, known to precede axonal atrophy in this mTBI model, two doses of 40 ng and 100 ng of LIF were administered twice daily, 5 days/week for two consecutive weeks. Sensorimotor functions were assessed at 4 and 8 weeks post mTBI, followed by ex-vivo brain magnetic resonance imaging at 9.4T and histopathology. mTBI mice showed sensorimotor deficits at 4 weeks, which worsened by 8 weeks post-injury. IN-LIF treatment prevented the progressive sensorimotor loss seen in the vehicle-treated controls. Increased mean diffusivity and decreased fractional anisotropy were observed in the corpus callosum and prefrontal cortex of mTBI brains. In a dose-dependent manner, IN-LIF prevented the mTBI-induced mean diffusivity increase and fractional anisotropy decrease. Histologically, there was significantly less astrogliosis, microgliosis and axonal injury in the IN-LIF treated mice vs. controls. These results support the therapeutic potential of IN-LIF to reduce delayed neurodegeneration and improve neurological outcomes after mTBIs.

neuroscience↗

bioRxiv2025

Genetic surveillance of Plasmodium-Anopheles compatibility markers during Anopheles stephensi associated malaria outbreak

Despite previous decline of malaria in Ethiopia, an outbreak in Dire Dawa in 2022 implicated invasive vector An. stephensi as responsible. The transmission of Plasmodium by invasive An. stephensi raises questions about the molecular basis of compatibility, and the origin of the Plasmodium being transmitted. The Plasmodium P47 gene is a parasite-vector interaction gene in Anopheles, and along with corresponding mosquito P47 receptor (P47Rec), can be critical in establishment of Plasmodium infections in anophelines. Here, we analyzed P47 and P47Rec sequences to determine the origin of Plasmodium detected in An. stephensi during the outbreak and evaluate markers of compatibility. Analysis of geographically informative SNPs in Pfs47 revealed that these P. falciparum exhibit the African haplotype. We also identified a single amino acid change in P47Rec within these An. stephensi, which could act as a marker for the propensity of An. stephensi populations to outbreaks. Together, we provide the basis for further study to deepen the understanding of invasive An. stephensi -African Plasmodium interactions to better control transmission of malaria and prevent further outbreaks.

evolutionary biology↗

bioRxiv2025

High-quality genome assembly for the genetically improved Abbassa Nile tilapia enables the reconstruction of X and Y haplotypes

The success of the Nile tilapia (Oreochromis niloticus) as an aquaculture species is partly the result of continuous selective breeding leading to high performing strains. These elite strains have been derived from breeding populations of diverse origins and crosses with other Oreochromis species. Owing to the complex and unique evolutionary histories of each strain, existing reference genomes of wild populations are unsuitable to implement genomic selection for beneficial traits such as growth or environmental resilience in aquaculture programmes. Here we generated a high-quality genome assembly and annotation of the WorldFish Genetically Improved Abbassa Nile tilapia (GIANT) elite strain using a combination of PacBio HiFi, and Omni-C Illumina sequencing. As a male Abbassa Nile tilapia was used for the generation of the genome assembly, we reconstructed both X and Y haplotypes, identifying both amhY and amh{Delta}y on LG23 indicating that Abbassa likely shares the same sex determination system as GIFT, and thereby differs from the existing reference genome, whose sex determination loci are located on LG1.

genomics↗

bioRxiv2025

Improved Graph-based Antibody-aware Epitope Prediction with Protein Language Model-based Embeddings

The accurate identification of B-cell epitopes is critical in antibody design, diagnostics, and immunotherapies. Many in silico approaches have recently been proposed to predict epitopes, but these approaches struggle primarily because of the variational and conformational nature of epitopes. However, deep learning-based approaches have recently shown great promise in achieving better performance at the epitope prediction task. In this paper, we employ a graph convolutional network (GCN) coupled with pre-trained protein language model (PLM)-based embeddings for epitope prediction on a benchmark antibody-specific epitope prediction (AsEP) dataset. We explore the use of different PLM-embedding methods on the epitope prediction task and show that the choice of PLM embeddings impacts the performance. Specifically, we find that antibody-specific PLMs such as AntiBERTy and general PLMs such as ProtTrans and ESM-2 for antigens provide improved epitope prediction performance with an AUCROC of 0.65, precision of 0.28, and recall of 0.46. The source code is available at: https://github.com/mansoor181/walle-pp.git.

bioinformatics↗

bioRxiv2024

Butyrophilin 2A2 promotes T cell immunoregulation by enhancing CD45 phosphatase activity within the immune synapse

B7 costimulatory family member Butyrophilin 2A2 (BTN2A2) is predominantly expressed by antigen presenting cells and regulates T cell immunity, but molecular mechanisms are unclear. Using immunoblots analyzing TCR-initiated signaling intermediaries, co-immunoprecipitation studies, confocal microscopy, structural modeling-guided mutational analyses, and microscale thermophoresis, we demonstrate that BTN2A2 directly interacts with CD45RO, resulting in CD45 retention within the immune synapse during TCR activation. Recombinant BTN2A2 increased murine CD4+Foxp3+ regulatory T cells (Treg) and reduced T helper 17 (Th17) cells in vitro through mechanisms dependent on CD45 phosphatase activity. BTN2A2 treatment reduced clinical expression of two murine autoimmune disease models and increased Treg/Th17 ratios. Analyses of BTN2A2-deficient animals showed exacerbated disease associated with reduced Treg/Th17 ratios. Addition of BTN2A2 to human mixed lymphocyte responses similarly enhanced human Treg and suppressed Th17 cells and was CD45 phosphatase dependent. Together, our studies identify BTN2A2 as a physiological CD45RO ligand that enhances CD45 phosphatase activity in murine and human T cells, providing mechanisms for BTN2A2-mediated amelioration of autoimmunity. SummaryButyrophilin 2A2 ameliorates autoimmunity by binding to CD45RO on activated T cell surfaces leading to dampened TCR signaling which in turn leads to expansion of T regulatory cells and reduction of Th17 differentiation.

immunology↗

bioRxiv2024

High-throughput Generation of Collagen Microbeads for 3D Cell Culture and Extracellular Vesicle Production

Collagen type I, a fundamental component of natural extracellular matrices across species, is an attractive material for the development of tissue engineering constructs. Nevertheless, the poor mechanical properties and thermal instability hinder its use for specific construction of 3D cultures and 3D printing. In this study, we present a microfluidic high-throughput approach for producing high-quality and uniform collagen microbeads without introducing any chemical modification. We achieved rapid and uniform collagen droplet fabrication with sizes spanning from 50 {micro}m to 1200 {micro}m in a production rate of up to 10000 droplets per minute. The resulting collagen microbeads can serve as numerous microbioreactors which are suspended in the culture medium without precipitation and are ideal for 3D cell growth. We demonstrated excellent cell compatibility, facilitating cell attachment and proliferation, as well as promoting extracellular vesicle secretion from collagen microbeads. This technology is facile and versatile for high throughput 3D cell culture, heterogeneous tissue modeling, and extracellular vesicle production, which is essential in drug delivery and drug screening.

bioengineering↗

bioRxiv2024

Comparative proteomics of biofilm development in Pseudoalteromonas tunicata discovers a distinct family of Ca2+-dependent adhesins

The marine bacterium, Pseudoalteromonas tunicata, is a useful model for studying mechanisms of biofilm development due to its ability to colonize and form biofilms on a variety of marine and eukaryotic host-associated surfaces. However, the pathways responsible for P. tunicata biofilm formation are still incompletely understood, in part due to a lack of functional information for a large proportion of its proteome. Here, we used comparative shotgun proteomics to examine P. tunicata biofilm development throughout the planktonic phase to three stages of biofilm development at 24, 48, and 72 h. Proteomic analysis identified 232 proteins that were up-regulated during different stages of biofilm development, including many hypothetical proteins as well as proteins known to be important for P. tunicata biofilm development such as the autocidal enzyme AlpP, violacein proteins, S-layer protein SLR4, and various pili proteins. We further investigated the top identified biofilm-associated protein (Bap), a previously uncharacterized 1600 amino acid protein (EAR30327), which we designated as "BapP". Based on AlphaFold modeling and genomic context analysis, we predicted BapP as a distinct Ca2+-dependent biofilm adhesin. Consistent with this prediction, a {Delta}bapP knockout mutant was defective in forming both pellicle and surface-associated biofilms, which was rescued by re-insertion of bapP into the genome. Similar to mechanisms of RTX adhesins, BapP-mediated biofilm formation was influenced by Ca2+ levels, and BapP is likely exported by a type 1 secretion system. Ultimately, our work not only provides a useful proteomic dataset for studying biofilm development in an ecologically relevant organism, but it also adds to our knowledge of bacterial adhesin diversity, emphasizing Bap-like proteins as widespread determinants of biofilm formation in bacteria.

microbiology↗

bioRxiv2024

Preserving Hidden Hierarchical Structure: Poincare Distance for Enhanced Genomic Sequence Analysis

The analysis of large volumes of molecular (genomic, proteomic, etc.) sequences has become a significant research field, especially after the recent coronavirus pandemic. Although it has proven beneficial to sequence analysis, machine learning (ML) is not without its difficulties, particularly when the feature space becomes highly dimensional. While most ML models operate with the conventional Euclidean distance, the hidden hierarchical structure present among a set of phylogenetically related sequences is difficult to represent in Euclidean space without losing much information or requiring many dimensions. Since such hierarchical structure can be informative to analysis tasks such as clustering and classification, we propose two measures for generating a distance matrix from a set of sequences based on distance in the Poincare disk model of hyperbolic geometry, or the Poincare distance, for short. Such a distance measure can allow to embedding of even a fully resolved phylogenetic tree in just two dimensions with minimal distortion to any hierarchical structure. Our first approach is based purely on the classical Poincare distance, while the other approach modifies this distance by combining the Euclidean norms and the dot product between the sequence representations. A thorough analysis of both measures demonstrates its superiority in a variety of genomic and proteomic sequence classification tasks in terms of efficiency, accuracy, predictive performance, and the capacity to capture significant sequence correlations. These approaches perform better than existing state-of-the-art methods across the majority of evaluation metrics.

genomics↗

bioRxiv2024

Compression and k-mer based Approach For Anticancer Peptide Analysis

Our research delves into the imperative realm of anti-cancer peptide sequence analysis, an essential domain for biological researchers. Presently, neural network-based methodologies, while exhibiting precision, encounter challenges with a substantial parameter count and extensive data requirements. The recently proposed method to compute the pairwise distance between the sequences using the compression-based approach [26] focuses on compressing entire sequences, potentially overlooking intricate neighboring information for individual characters (i.e., amino acids in the case of protein and nucleotide in the case of nucleotide) within a sequence. The importance of neighboring information lies in its ability to provide context and enhance understanding at a finer level within the sequences being analyzed. Our study advocates an innovative paradigm, where we integrate classical compression algorithms, such as Gzip, with a pioneering k-mersbased strategy in an incremental fashion. Diverging from conventional techniques, our method entails compressing individual k-mers and incrementally constructing the compression for subsequences, ensuring more careful consideration of neighboring information for each character. Our proposed method improves classification performance without necessitating custom features or pre-trained models. Our approach unifies compression, Normalized Compression Distance, and k-mers-based techniques to generate embeddings, which are then used for classification. This synergy facilitates a nuanced understanding of cancer sequences, surpassing state-of-the-art methods in predictive accuracy on the Anti-Cancer Peptides dataset. Moreover, our methodology provides a practical and efficient alternative to computationally demanding Deep Neural Networks (DNNs), proving effective even in low-resource environments.

bioinformatics↗

bioRxiv2024

A Comprehensive Pangenome Approach to Exploring the Probiotic Potential of Weissella Confusa

BackgroundFermented foods harbour the bacterium Weissella confusa, which has probiotic properties but can potentially act as an opportunistic pathogen in humans and animals. Using pangenome analysis, our study aimed to improve the classification and identify functional traits of publicly available W. confusa genomes, focusing on evaluating their potential as probiotics. MethodThe genomic sequence of 120 strains of W. confusa was acquired from the NCBI RefSeq database. The downloaded sequences underwent a quality verification and filtering process. Ultimately, the chosen genomes were examined for comparative genomic analysis. We employed Roary to investigate the pangenome of W. confusa. Fishers exact test was utilised to analyse contingency tables, with a significance threshold of p < 0.05 (two-tailed). ResultsOur investigation revealed that the pangenome of W. confusa comprises 1100 core genes, 184 soft-core genes, 1407 shell genes, and 7006 cloud genes. This finding emphasises the "open" aspect of the W. confusa pangenome. The comparison of genomes showed that there were no acquired antibiotic resistance genes. However, the strains had different amounts of prophage regions, CRISPR arrays, and plasmids. Our research identified probiotic marker genes (PMGs), with the majority (78%) found in the core and soft-core genomes of various strains of W. confusa. ConclusionsAn extensive investigation of the W. confusa pangenome has concluded that it could be useful as a probiotic. Additional research is necessary to thoroughly evaluate the potential risks.

bioinformatics↗

bioRxiv2024

Oxytocin Gαi signaling-induced amygdala astrocytes processes retraction shapes behavioral stress response

Anticipated reactions to stressful situations are vital for the survival and well-being of organisms, and abnormal reactions are involved in stress-related disorders. The neuropeptide oxytocin is a key modulator ensuring well-adapted stress responses. Oxytocin acts on both neurons and astrocytes, but the molecular and cellular mechanisms mediating stress response remain poorly understood. Here, we focus on the amygdala, a crucial hub that integrates and processes sensory information through oxytocin- dependent mechanisms. Using an acute stress paradigm in mice, genetic and pharmacological manipulations combined with proteomic, morphological, electrophysiological and behavioral approaches, we reveal that oxytocinergic modulation of the freezing response to stress is mediated by transient Gi-dependent retraction of astrocytic processes, followed by enhanced neuronal sensitivity to extracellular potassium in the amygdala. Our findings elucidate a pivotal role for astrocytes morphology- dependent modulation of brain circuits that is required for proper anticipated behavioral response to stressful situations.

neuroscience↗

bioRxiv2024

Designing and bioengineering of CDRs with higher affinity against receptor-binding domain (RBD) of SARS-CoV-2 Omicron variant

The emergence of the SARS-CoV-2 Omicron variant highlights the need for innovative strategies to address evolving viral threats. This study bioengineered three nanobodies H11-H4, C5, and H3 originally targeting the Wuhan RBD, to bind more effectively to the Omicron RBD. A structure-based in silico affinity maturation pipeline was developed to enhance their binding affinities. The pipeline consists of three key steps: high-throughput in silico mutagenesis of complementarity-determining regions (CDRs), protein-protein docking for screening, and molecular dynamics (MD) simulations for assessment of the complex stability. A total of 741, 551, and 684 mutations were introduced in H11-H4, C5, and H3 nanobodies, respectively. Protein-protein docking and MD simulations shortlisted high-affinity mutants for H11-H4(6), C5(5), and H3(6). Further, recombinant production of H11-H4 mutants and Omicron RBD enabled experimental validation through Isothermal Titration Calorimetry (ITC). The H11-H4 mutants R27E, S57D, S107K, D108W, and A110I exhibited improved binding affinities with dissociation constant (KD) values ranging from [~]8.8 to [~]27 {micro}M, compared to the H11-H4 nanobody KD of [~]32 {micro}M, representing a three-fold enhancement. This study demonstrates the potential of the developed in silico affinity maturation pipeline as a rapid, cost-effective method for repurposing nanobodies, aiding the development of robust prophylactic strategies against evolving SARS-CoV-2 variants and other pathogens. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/613860v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1ea8ff7org.highwire.dtl.DTLVardef@91d567org.highwire.dtl.DTLVardef@51629corg.highwire.dtl.DTLVardef@1212752_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

Bacterial genome encoded ParMs

ParMs generally exist on low copy number plasmids where they contribute to plasmid segregation and stable inheritance. We carried out bioinformatics analysis, which indicated that ParM genes are not only confined to plasmids but are also occasionally found on genomes. Here we report the discovery and characterization of two chromosome encoded ParMs (cParMs) from the genomes of Desulfitobacterium hafniense (Dh-cParM1) and Clostridium botulinum (Cb-cParM). Both cParMs form filaments, exhibit nucleotide hydrolysis, and possess characteristic ParM subunit structures. Dh-cParM1 forms single and tightly coupled double filaments and is highly conserved on the chromosomes of five of six Desulfitobacterium species. Interestingly, these bacteria have not been reported to harbour plasmids. Cb-cParM possesses unique properties. Its filaments were stable after nucleotide hydrolysis and Pi release, and its ParR, (Cb-cParR) did not affect the initial stage of Cb-cParM polymerization but displayed properties of a depolymerization factor for mature filaments. These results indicate functional, polymerizing ParMs can be encoded on genomes, suggesting that ParM roles may extend to other functions beyond plasmid segregation.

microbiology↗

bioRxiv2024

DeepPWM-BindingNet: Unleashing Binding Predictionwith Combined Sequence and PWM Features

A crucial challenge in molecular biology is the prediction of DNA-protein binding interactions, which has applications in the study of gene regulation and genome functionality. In this paper, we present a novel deep-learning framework to predict DNA-protein binding interactions with increased precision and interoperability. Our proposed framework DeepPWM-BindingNet leverages the rich information encoded in Position Weight Matrices (PWMs), which capture the sequence-specific binding preferences of proteins. These PWM-derived features are seamlessly integrated into a hybrid model of convolutional recurrent neural networks (CRNNs) that extracts hierarchical features from DNA sequences and protein structures. The sequential dependencies within the sequences are captured by recurrent layers. By incorporating PWM-derived features, the models interpretability is improved, enabling researchers to learn more about the underlying binding mechanisms. The models capacity to locate crucial binding sites is improved by the incorporation of an attention mechanism that highlights crucial regions. Experiments on diverse DNA-protein interaction datasets demonstrate the proposed approach improves the predictive performance. The proposed model holds significant potential in deciphering intricate DNA-protein interactions, ultimately advancing our comprehension of gene regulation mechanisms.

bioinformatics↗

bioRxiv2024

Impact of Blended NPSB Fertilizer Rates on Sugarcane (Saccharum officinarum L.) Varieties: Seed Cane Yield and Quality Insights from North Western Ethiopia

Sugarcane primarily cultivated for sugar production and other multiple uses. A field experiment was conducted at Tana Beles sugar project, North western Ethiopia during 2021 and 2022 cropping season to determine the optimum rate of NPSB fertilizer rate on three sugarcane varieties. The treatments were laid out in factorial randomized complete block design arranged with three replications. The experiment was arranged with five levels of NPSB blended fertilizer (0, 200, 260, 320 and 380 kg ha-1) combined with three sugarcane varieties (NCO-334, N-14 and C86/56). Among the parameters of seed cane crop; germination percent stalk weight, stalk diameter, node length, inter node number, plant height, stalk population, and sett yield for growth and yield parameters and sett moisture content, sett nitrogen content, reducing sugar content and total sugar content for seed cane quality parameters significantly affected by applied NPSB fertilizer, varieties and their interaction (p<0.05). Brix% and pol% were not significantly affected by different rates of NPSB fertilizer rates and varieties (p<0.05). The highest leaf area index, plant population, sett yield, average cane weight, seed cane moisture content and total nitrogen content was attained with 380 kg ha-1NPSB fertilizer applied on variety NCO-334 and N-14. Maximum population stand, average plant height, and sett yield of verity C86/56 were recorded at 320 kg ha-1 NPSB fertilizer level. Sett yield, were positively correlated with germination percent, population stand count, inter node number, seed cane weight, seed cane diameter reducing sugar content, total sugar yield and sett moisture content. Therefore, it is advisable to recommend 380 kg NPSB ha1 for variety NCO-334 and N-14, and 320 kg NPSB ha1 for variety C86/56 with application of 160 kg ha-1 urea at the age of two month and half for effective seed cane production. It was aimed to fill the seed cane fertilizer rate problems of different sugarcane varieties.

plant biology↗

bioRxiv2024

Nanopore-based analysis unravels the genetic landscape and phylogenetic placement of human-infecting Trichuris species in Cote d'Ivoire, Tanzania, Uganda, and Laos

Soil-transmitted helminthiases, particularly trichuriasis, affect over 500 million people, mostly in low- and middle-income countries. Traditional diagnostics fail to distinguish between Trichuris species, obscuring transmission patterns and treatment outcomes. Using nanopore-based full-length ITS2 rDNA sequencing, we analyzed 687 samples from Cote dIvoire, Laos, Tanzania, and Uganda, confirming the phylogenetic placement of Trichuris trichiura and the recently described Trichuris incognita. We identified two genetically distinct Trichuris species infecting humans, with divergent geographic patterns and presence in non-human primates, suggesting complex host-parasite dynamics. Within-country genetic variation indicated local adaptation and cryptic population structure. Importantly, we demonstrated that ITS2 fragment length is a robust, cost-effective diagnostic marker for differentiating T. incognita and T. trichiura, offering a practical alternative to sequencing for resource-limited settings. These findings expose the hidden complexity of Trichuris infections and highlight the urgent need to update diagnostic and control strategies to account for overlooked species diversity in endemic regions.

genomics↗