Biology subjects

Khan, R.

Publications and source records attributed to Khan, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Aspartate transaminases are required for blood development

Red blood cells (RBCs) have a limited lifespan of approximately 120 days. This necessitates continuous RBC production, resulting in [~]200 billion new RBCs made per day to maintain oxygen delivery. Despite this enormous biosynthetic demand, the metabolic pathways supporting erythropoiesis are poorly understood. We profiled metabolites across four independent models of elevated erythropoiesis and a consistent increase in aspartate levels emerged when compared to controls. This suggested a potential role for aspartate metabolism in RBC production. To test this, we deleted the aspartate aminotransferases Got1 or Got2 globally or in an erythroid-specific manner. Loss of either enzyme resulted in anemia, with Got2 deficiency producing a more severe phenotype. Individual loss of either Got or dual Got deletion led to an erythroid defect, where early progenitors accumulated. In human and mouse models of erythropoiesis, GOT1 and GOT2 loss had opposing impacts on aspartate despite exhibiting similar anemic phenotypes, suggesting an aspartate-independent function for these enzymes. GOT1 and GOT2 are also components of the malate-aspartate shuttle (MAS), which regulates NAD(H) homeostasis. However, conditional deletion of another MAS enzyme, Mdh1, did not cause anemia, and alleviating NADH reductive stress in GOT2-deficient cells with cytoplasmic bacterial NADH oxidase (LbNOX) failed to restore erythropoiesis. Instead, transcriptomic and epigenetic analyses revealed dysregulation of chromatin histone modifications in GOT-deficient erythroid cells, implicating epigenetic dysfunction as a driver of defective erythropoiesis. Collectively, these findings identify a previously unrecognized role for GOT1 and GOT2 as a central link between metabolism and epigenetic regulation during erythroid development. These insights may inform the development of new therapeutic strategies for anemia.

biochemistry↗

bioRxiv2025

Poly(ADP-ribose) Polymerase 1 Deficiency Attenuates Amyloid Pathology, Neurodegeneration, and Cognitive Decline in a Familial Alzheimer Disease Model

Poly(ADP-ribose) (PAR) polymerase-1 (PARP1) has been implicated in DNA damage responses and neuroinflammation in Alzheimers disease (AD), yet its role in amyloid-{beta} (A{beta}) pathology remains unclear. Here, we show that PARP1 activation drives A{beta} pathology and neurodegeneration. Using a sensitive ELISA, we observed significantly elevated PAR levels in the cerebrospinal fluid (CSF) of patients with mild cognitive impairment (MCI) and AD compared to controls. In vitro, oligomeric A{beta}1-42 activated PARP1 and induced DNA damage, while genetic or pharmacological inhibition of PARP1 conferred neuroprotection. In vivo, PARP1 knockout in the 5XFAD mouse model of amyloidosis led to reduced amyloid plaque burden, preserved synaptic and neuronal integrity, attenuated glial activation and neuroinflammation, and rescued cognitive deficits. Mechanistically, PARP1 deficiency decreased amyloid precursor protein (APP) and BACE1 levels, altered {gamma}-secretase complex composition, and enhanced A{beta} degradation via neprilysin. These findings position PARP1 as a critical mediator of A{beta} toxicity and neurodegeneration, suggesting its inhibition as a promising therapeutic strategy for AD. Significance StatementOur study identifies poly(ADP-ribose) (PAR) as an elevated biomarker in the cerebrospinal fluid of patients with mild cognitive impairment and Alzheimers disease, correlating with established markers of amyloid pathology. We demonstrate that PARP1, the enzyme responsible for PAR synthesis, is activated by neurotoxic A{beta}1-42 and mediates neuronal death, amyloid plaque formation, neuroinflammation, and cognitive deficits in a mouse model of AD. Importantly, genetic ablation of PARP1 not only protects neurons from A{beta} toxicity but also reduces amyloid burden by suppressing A{beta} production and enhancing its degradation. These findings highlight PARP1 as a critical regulator of amyloid pathology and neurodegeneration, and suggest that PARP1 inhibition may offer a promising therapeutic avenue for Alzheimers disease by simultaneously targeting multiple pathogenic mechanisms.

neuroscience↗

bioRxiv2025

Quantum analysis of protein-ligand binding by integrating structural resolution, sequence homology, and ligand properties

Predicting protein-ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we present an integrative study that combines classical machine learning and quantum-enhanced modeling to investigate how crystal structure resolution, sequence similarity, and ligand properties jointly influence binding affinity. Using a curated "refined" dataset from PDBbind and an expanded general dataset, we first conduct correlation and regression analyses to quantify the relationships among binding affinity, ligand descriptors (e.g., molecular weight, logP), and protein structural metrics (resolution, R-factor). We observe moderate positive correlations between ligand size/hydrophobicity and affinity, and a slight negative correlation between resolution and affinity in the refined dataset that largely disappears in the general set. We then train multiple predictive models, including random forests, deep neural networks, and quantum-enhanced approaches--quantum kernel methods, variational quantum circuits, and a hybrid classical-quantum neural network. Experimental results show that quantum-enhanced models perform on par with classical methods in predicting binding affinities and, in some cases, offer modest improvements. Notably, a hybrid quantum-classical model achieves the highest accuracy (Pearson correlation R{approx}0.80R) on the refined dataset. These findings highlight the potential of quantum computing for capturing complex patterns in biomolecular data, laying groundwork for improved structure-based drug design. Our study underscores that while data quality and curation greatly influence observed trends, quantum machine learning despite current hardware limitations can already serve as a competitive and promising tool in computational structural biology. AUTHOR SUMMARYIn our study, we confront a major bottleneck in the creation of new medicines: accurately predicting how strongly a potential drug will attach to its target protein in the body. Getting this right early in the process could save billions of dollars and years of research by preventing dead-ends. We investigated if quantum computing, a new technology that processes information in a fundamentally different way, could provide a better solution. We trained several computer models to predict these binding strengths, comparing standard artificial intelligence (AI) with new models enhanced by quantum machine learning. Our results showed that a hybrid model, which strategically combines classical AI with a quantum-powered component, delivered the most accurate predictions on high-quality, curated data. This work demonstrates that quantum computing is already a competitive tool for real-world biological problems, not just a future possibility. We believe that by further developing these quantum-enhanced approaches, we can create more reliable predictive tools to make the long and difficult search for new drugs faster and more successful.

molecular biology↗

bioRxiv2025

A chromosome-length reference genome for the common side-blotched lizard Uta stansburiana and gene expression data reveals fast pace-of-life comes with environmental stability

Uta stansburiana are an emerging model system for testing hypotheses regarding the evolution of pace-of-life syndromes (POLS) across its variable environments and wide latitudinal gradient. POLS are suites of traits related to variation of life history along a slow maturing-fast maturing continuum. We present a high-quality chromosome-level reference genome for U. stansburiana and use RNA-seq gene expression data to test for molecular correlates for pace-of-life differences between locations with higher and lower climate seasonality, UV differences, and sexual size dimorphism (SSD). Our assembly is 2.1 Gbp, has scaffold N50 of 320 Mbp, includes 104 scaffolds, and has an L50 of 3. The assembly comprises six macrochromosomes and 11 microchromosomes. We annotated 20,350 genes for the assembly and found a repeat element composition of 49.23%, similar to work in other phrynosomatid lizards. RNA-seq data reveal differential expression in genes consistent with pace-of-life differences and physiological variation including those related to stress, sexual reproduction, and cell proliferation/carcinogenesis between distinctive environments. Our results provide genes potentially underlying the molecular bases of POLS differences in a wild lizard.

evolutionary biology↗

bioRxiv2025

Proximate Composition and Mineral Analysis of Housefly Maggots and Mealworms Compared to Fish and Soybean Meals

The nutritional profiles of dried yellow mealworms and housefly maggots were analyzed and compared with conventional protein sources in poultry diets such as fish meal (FM) and soybean meal (SBM). Nutrient composition was determined through proximate analysis, while amino acid and mineral contents were analyzed using HPLC-based amino acid analyzer and atomic absorption spectrophotometer, respectively. Metabolizable energy (ME) was calculated to estimate energy content per kilogram of feed. The crude protein content of housefly maggots (57.8%) was comparable to that of fish meal (58.5%), which was the highest among the evaluated feeds, while mealworms had a slightly lower protein content (51.02%). The highest ether extract values were recorded for mealworms (27.3%) and maggots (26.9%), surpassing those in FM and SBM. The highest ME was calculated for housefly maggots (4,074 kcal/kg), followed by mealworms (3,988 kcal/kg) and FM (3,824 kcal/kg). Mineral profiling showed the highest calcium content in mealworms (5.23 g/kg), followed by maggots (5.13 g/kg). Phosphorus and zinc were recorded highest in housefly maggots, at 16.8 g/kg and 98.5 mg/kg, respectively. The limiting amino acids lysine, leucine, and isoleucine were highest in housefly maggots, followed by FM and mealworms, showing comparable values between the insect meals and FM. Methionine content was recorded highest in mealworms (2.3 g/100g), closely followed by housefly maggots (2.27 g/100g). The study demonstrated that housefly maggots and mealworms have a nutritive profile superior to soybean meal and comparable to fish meal.

zoology↗

bioRxiv2025

Neuropathological hallmarks during the chronic phase of ischemic stroke in mice and humans

BackgroundImprovements in acute stroke treatment, including endovascular thrombectomy and critical care management, have increased survival rates post-stroke. However, stroke remains a leading cause of long-term disability and many survivors have significant neurological and cognitive deficits. Despite this, the chronic neurological sequelae and underlying secondary injury mechanisms induced by ischemic stroke remain understudied. MethodsThis study examined long-term neurobehavioral recovery and neuropathology at 2- and 6- months post-stroke in young (12-week-old) male C57Bl/6 mice after a 60-minute transient middle cerebral artery occlusion (MCAO) or sham surgery. Behavioral testing included the open field test (OFT), novel object recognition test (NORT), fear conditioning (FC), nesting activity, and tail suspension. Post-mortem brain samples from patients with chronic ischemic stroke were also assessed. Immunohistochemistry (IHC) was performed to assess demyelination (MBP), neuronal apoptosis (TUNEL), and A{beta}42 in human brains. Flow cytometric analysis was performed to assess microglial phenotypes, the chronic neuroimmune landscape, and to evaluate senescent-like phenotypes (SA-{beta}Gal and lipofuscin). Transcriptomic profiling was performed using RNA isolated from the ipsilateral hemisphere in stroke mice. ResultsExperimental stroke caused progressive cognitive and motor decline up to 6 months post-MCAO. IHC and flow cytometric analyses revealed a significant increase in TUNEL-positive neurons, cortical and hippocampal gliosis, white matter degradation, senescent cell accumulation, and altered microglial function. IHC analysis of postmortem human brains shows significantly increased levels of microgliosis, senescent cells and amyloid burden. Transcriptomic analysis revealed that pathways involving apoptosis, microglial activation and the complement pathway were chronically upregulated after stroke. ConclusionOur findings demonstrate that ischemic stroke induces a non-resolving microglial response and accelerated inflamm-aging in the brain, evidenced by premature senescence and elevated production of cytokines within the chronic infarct microenvironment. Senescent-like phenotypes and chronic neurodegenerative disease signatures may contribute to the progressive worsening of cognitive function post-stroke. These results suggest that chronic, ongoing neurodegeneration occurs late after stroke, even in younger mice. Mitigating these detrimental changes may offer viable targets for delayed treatment strategies for stroke.

neuroscience↗

bioRxiv2025

Predictive Coding Explains Asymmetric Connectivity in the Brain: A Neural Network Study

Seminal frameworks of predictive coding propose a hierarchy of generative modules, each attempting to infer the neural representation of the module one level below; the predictions are carried by top-down feedback projections, while the predictive error is propagated by reciprocal forward pathways. Such symmetric feedback connections support visual processing of noisy stimuli in computational models. However, neurophysiological studies have yielded evidence of asymmetric cortical feedback connections. We investigated the contribution of neural feedback during sensorimotor processes, in particular visual processing during grasp planning, by utilizing convolutional neural network models that had been augmented with predictive feedback and were trained to compute grasp positions for real-world objects. After establishing an ameliorative effect of symmetric feedback on grasp detection performance when evaluated on noisy stimuli, we characterized the performance effects of asymmetric feedback, similar to that observed in the cortex. Specifically, we tested model variants extended with short-, medium- and long-range feedback connections (i) originating at the same source layer or (ii) terminating at the same target layer. We found that the performance-enhancing effect of predictive coding under adverse conditions was optimal for medium-range asymmetric feedback. Moreover, this effect was most prominent when medium-range feedback originated at a level of representational abstraction that was proximal to the input layer, in contrast to more distal layers. To conclude, our simulations show that introducing biologically realistic asymmetric predictive feedback improves model robustness to noisy visual stimuli in a neural network model optimized for grasp detection. Significance statementIt is commonly held that the brain predicts the causes of its sensorium via top-down neural pathways. While canonical models of predictive coding assume reciprocal feedforward and feedback connections, functional evidence highlights the importance of non-reciprocal asymmetric feedback, whose role remains poorly understood, particularly in sensorimotor functions. Using neural network models of grasp planning, we characterized optimal pathlengths and source regions for asymmetric feedback facilitating visuomotor processing of noisy sensory inputs. Our findings show that medium-range feedback from early layers marks a sweet spot, incorporating optimal distance between the neural representations of source/target layers and representational abstraction of the feedback source. This intimates an uncharted role of intermediate brain areas along the visuomotor stream as a source of predictive signals.

neuroscience↗

bioRxiv2025

SCGclust: Single Cell Graph clustering using graphautoencoders integrating SNVs and CNAs

Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognosis and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and responses to drug and treatment. While the low coverage scDNA-seq technologies typically provides a large number of cells, accurate cell clustering is essential for effectively characterizing ITH. Existing cell clustering methods typically are based on either single nucleotide variations (SNV) or copy number alterations (CNA), without leveraging both signals together. Since both SNVs and CNAs are indicative of the cell subclonality, in this paper, we designed a robust cell clustering tool that integrates both signals using a graph autoencoder. Our model co-trains the graph autoencoder and a graph convolutional network (GCN) to guanrantee meaningful clustering results and to prevent all cells from collapsing into a single cluster. Given the low dimensional embedding generated by the autoencoder, we adopted a Gaussian Mixture Model to further cluster cells. We evaluated our method on eight simulated datasets and a real cancer sample. Our results demonstrate that our method consistently achieves higher V-measure scores compared to SBMClone, a SNV-based method, and a K-means method, which relies solely on CNA signals. These findings highlight the advantage of integrating both SNV and CNA signals within a graph autoencoder framework for accurate cell clustering. SCGclust is publicly available at https://github.com/compbio-mallory/cellClustering_GNN.

bioinformatics↗

bioRxiv2025

Disrupted Sphingosine-1-Phosphate Homeostasis Drives Nephrotoxicity in Sphingosine-1-Phosphate Lyase Insufficiency Syndrome (SPLIS)

Sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS), also known as nephrotic syndrome type 14 (NPHS14), is an autosomal recessive disorder characterized by renal, neurological, dermatological, endocrine, and immunological symptoms. This condition is caused by loss-of-function mutations in the SGPL1 gene, which encodes sphingosine-1-phosphate lyase (SGPL1p/SPL), the enzyme responsible for the terminal degradation of sphingosine-1-phosphate (S1P) in sphingolipid catabolism. We investigated a novel case of SPLIS associated with a recently reported SGPL1 mutation (c.1084T>A; p.Ser362Thr). Using stable isotope flux analyses, we demonstrated in patient-derived fibroblasts and HEK293T SGPL1 knockout models that SGPL1p deficiency does not consistently result in pathological S1P accumulation. Instead, SPL-deficient cells are able to maintain steady-state S1P levels through two compensatory mechanisms: O_LIRegulation of de novo sphingolipid synthesis via the ORMDL-ceramide axis. C_LIO_LIIncreased conversion of excess ceramides into glycosphingolipids. C_LI However, when steady-state conditions are disrupted--either by external sphingolipid supplementation or by impairing homeostatic control--a pathological increase in intracellular S1P occurs in SPL-deficient cells. In vivo, Sgpl1-/-mice exhibited significant urinary excretion of S1P and marked S1P enrichment in the kidneys. This pathological accumulation of S1P dysregulates cytoskeletal homeostasis, impairing renal epithelial formation. Based on these findings, we hypothesize that the reabsorption of urinary S1P contributes to toxic renal accumulation, providing an explanation for the nephrotoxicity observed in SPLIS and its association with nephrotic syndrome. Importantly, we found that the cytoskeletal disruptions could be mitigated by inhibiting the Rho-ROCK signaling pathway using the clinically approved inhibitor Fasudil. These findings illuminate the pathophysiological basis of SPLIS nephrotoxicity and propose a promising pharmacological intervention strategy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/634100v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@e70e68org.highwire.dtl.DTLVardef@1630198org.highwire.dtl.DTLVardef@fde277org.highwire.dtl.DTLVardef@1f2b8e_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

HIV-1 subtype C LTR Sp1IIIT5A mutant enhances transcription activity and Sp1 binding affinity

BackgroundGenetic variation within HIV-1 subtype C (HIV-1C) long terminal repeat (LTR) transmitted/founder viruses influences transcription activation and clinical disease outcomes. The role of specific mutations such as thymine-to-adenine (T5A) mutation at position five of the Specificity protein 1 (Sp1) III motif (Sp1IIIT5A) remains underexplored. This study investigates the impact of Sp1IIIT5A on HIV-1C LTR transcription activity and Sp1 binding affinity. MethodsThe Sp1IIIT5A mutant and consensus HIV-1C LTR sequences were cloned into the pGL3 Luciferase Basic reporter vector, sequenced, and transfected into SVG and Jurkat cell lines, independently. Transcription activity and Sp1 expression were assessed via luciferase assays and Western blot. Structural models of Sp1IIIT5A, consensus LTRs and Sp1 were generated, and docking scores calculated using HDOCK, HADDOCK, and pyDockDNA. Molecular dynamics simulations analyzed stability and interactions of Sp1IIIT5A LTR-Sp1 complexes. Results and DiscussionThe Sp1III5A mutant significantly increased basal (SVG: p<0.0001; Jurkat: p=0.0052) and Tat-mediated (SVG and Jurkat: p<0.0001) HIV-1C LTR transcription activity in both cell lines, with stronger effects in SVG cells. Sp1 expression levels remained similar across cell lines (p=0.0814). Sp1III5A exhibited higher binding affinity (-332.7, -174.6, and -279.2 kcal/mol) than the canonical sequence (-311.4, -157.0, and -247.3 kcal/mol). ConclusionThe Sp1IIIT5A mutation significantly enhances HIV-1C LTR transcription activity and Sp1 binding affinity, indicating its potential tole in modulating HIV-1C transcription and pathogenesis. Further investigation is needed to elucidate its impact on HIV-1C latency. ImportanceIn this study we show that the thymine-to-adenine (T5A) mutation at position five of the Sp1 III motif (Sp1IIIT5A) within the HIV-1 subtype C (HIV-1C) long terminal repeat (LTR) increases viral transcription. This mutation enhances the interaction between HIV-1C and the cellular transcription factor Sp1, promoting the viral strains ability to replicate. Our findings provide insight into why certain HIV-1C strains behave differently, potentially leading to heterogenous rates of disease progression. Understanding the Sp1IIIT5A mutation could lead to improved strategies for controlling HIV-1C and developing cure strategies to clear the infection or result in virus remission.

microbiology↗

bioRxiv2024

IL-1β-driven NF-κB transcription of ACE2 as a Mechanism of Macrophage Infection by SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by infection with the enveloped RNA betacoronavirus, SARS-CoV-2, led to a global pandemic involving over 7 million deaths. Macrophage inflammatory responses impact COVID-19 severity; however, it is unclear whether macrophages are infected by SARS-CoV-2. We sought to identify mechanisms regulating macrophage expression of ACE2, the primary receptor for SARS-CoV-2, and to determine if macrophages are susceptible to productive infection. We developed a humanized ACE2 (hACE2) mouse whereby hACE2 cDNA was cloned into the mouse ACE2 locus under control of the native promoter. We validated the susceptibility of hACE2 mice to SARS-CoV-2 infection relative to wild-type mice and an established K18-hACE2 model of acute fulminating disease. Intranasal exposure to SARS-CoV-2 led to pulmonary consolidations with cellular infiltrate, edema, and hemorrhage, consistent with pneumonia, yet unlike the K18-hACE2 model, hACE2 mice survived and maintained stable weight. Infected hACE2 mice also exhibited a unique plasma chemokine, cytokine, and growth factor inflammatory signature relative to K18-hACE2 mice. Infected hACE2 mice demonstrated evidence of viral replication in infiltrating lung macrophages, and infection of macrophages in vitro revealed a transcriptional profile indicative of altered RNA and ribosomal processing machinery as well as activated cellular antiviral defense. Macrophage IL-1{beta}-driven NF-{kappa}B transcription of ACE2 was an important mechanism of dynamic ACE2 upregulation, promoting macrophage susceptibility to infection. Experimental models of COVID-19 that make use of native hACE2 expression will allow for mechanistic insight into factors that can either promote host resilience or increase susceptibility to worsening severity of infection.

immunology↗

bioRxiv2024

MortX: A Domain Generalization Benchmark for Mouse Cortex Segmentation and Registration

Mesoscale understanding of human brain development is crucial for understanding neurodevelopmental disorders. By applying AI techniques to analyze high-resolution, multi-modal brain imaging datasets across postnatal ages, researchers can study cortical development at the granular level. We introduce MortX, a benchmark dataset of the developing mouse cortex that captures multiple postnatal stages with annotations for distinct anatomical and functional subregions and layers. MortX features high-resolution imaging data including bright-field and fluorescence-labeled neuronal markers. We developed a standardized cortical atlas of genetic markers and manually registered it to brain section images for ground-truth labeling. The dataset serves as a benchmark for domain generalization in neuroimaging, enabling both classical and deep learning models to be trained on source brains and tested on unseen targets. Our results demonstrate generalized model performance and structural invariance across ages. We open-source MortX as a community resource for mouse brain segmentation and registration, emphasizing domain adaptation. This dataset addresses key challenges in mouse brain imaging and advances machine learning models that will help unravel neurodevelopmental disorders.

neuroscience↗

bioRxiv2024

Wound state monitoring by multiplexed, electrochemical, real-time, localized, inflammation-tracking nitric oxide sensor (MERLIN)

Nitric oxide (NO) released endogenously by induced nitric oxide synthase (iNOS) in macrophages is a key regulatory biomarker for wound inflammation. Detecting NO directly on the wound bed is challenging due to its short half-life time (6 - 50 s), low physiological concentration (nM - {micro}M) and interferences in the complex wound environment. Existing NO sensors suffer from limitations such as rigid substrates, single point of detection and complex measurement setup. Here we present a compliant, multiplexed, electrochemical, real-time, localized, inflammation-tracking nitric oxide sensor (MERLIN) array for in vivo spatiotemporal measurement of NO, with high sensitivity (15.6 {+/-} 5.0 nA/M for 1.5 mm diameter electrodes), selectivity against nitrites (ca. 27900-fold), ascorbic acid (ca. 3800-fold), and uric acid (ca. 6900-fold), and low limit of detection (8.00 nM). We developed a robust device fabrication protocol, comprehensive and highly reproducible in vitro characterization yielding a consistent device performance deployed for wound healing diagnostics. MERLIN spatiotemporally tracked NO on rat skin wounds for 7 days and results indicated that NO peaks on day 3, in line with previously reported iNOS activity. MERLIN allows spatial mapping of the NO gradient across the wound bed, which can be used to provide diagnostic information to assist wound care.

bioengineering↗

bioRxiv2024

The life history traits of phages in a cocktail determine coinfection dynamics and efficacy

Phage cocktails are preferred over single phages for efficacious and broader-spectrum therapy. An ideal phage cocktail should have a minimum number of phages with efficient infection kinetics and delay the emergence of resistance in bacterial populations. This study examined population dynamics of the common host and combinations of two phages (N4, KKE5P, and Ec_YwIITB1) through experimental and modeling approaches to gaining insights into how phage life history traits influence the outcome of infection in the short-term of approximately an infection cycle, and whether it is informative for developing efficacious cocktails. We tested the killing efficacy of a cocktail containing two divergent phages (N4 and Ec_YwIITB1) with similar adsorption rates but differed in their latency period. Because of the shorter latency period, phage N4 dominated under all conditions tested. The cocktail essentially behaves as a single phage. When two phages (N4 and KKE5P), with similar adsorption rates and latency periods but targeted different host receptors were used, it not only resulted in the efficient replication of both phages but also improved the suppression of the emergence of resistance when compared to the N4 and Ec_YwIITB1 combination, thus behaving like an ideal cocktail. The ODE-based mathematical model demonstrated predictive capabilities consistent with experimental observations and offered insights into infection dynamics. The model may aid in phage selection and optimizing cocktail formulation based on phage life history traits. This study highlights the need for thorough characterization of phage growth parameters and informed combinations of phages, as random combinations could lead to undesirable outcomes.

microbiology↗

bioRxiv2024

Deficiency in a special dynein DNAH12 causes male infertility by impairing DNAH1 and DNALI1 recruitment in humans and mice

Asthenoteratozoospermia, a prevalent cause of male infertility, lacks a well-defined etiology. DNAH12 is a special dynein featured by the absence of a microtubule-binding domain, however, its functions in spermatogenesis remain largely unknown. Through comprehensive genetic analyses involving whole-exome sequencing and subsequent Sanger sequencing on infertile patients and fertile controls from six distinct families, we unveiled six biallelic mutations in DNAH12 that co-segregate recessively with male infertility in the studied families. Transmission electron microscopy (TEM) revealed pronounced axonemal abnormalities, including inner dynein arms (IDAs) impairment and central pair (CP) loss in sperm flagella of the patients. Mouse models (Dnah12-/- and Dnah12mut/mut) were generated and recapitulated the reproductive defects in the patients. Noteworthy, DNAH12 deficiency did not show effects on cilium organization and function. Mechanistically, DNAH12 was confirmed to interact with two other IDA components DNALI1 and DNAH1, while disruption of DNAH12 leads to failed recruitment of DNALI1 and DNAH1 to IDAs and compromised sperm development. Furthermore, DNAH12 also interacts with radial spoke head proteins RSPH1, RSPH9, and DNAJB13 to regulate CP stability. Moreover, the infertility of Dnah12-/- mice could be overcome by intracytoplasmic sperm injection (ICSI) treatment. Collectively, DNAH12 plays a crucial role in the proper organization of axoneme in sperm flagella, but not cilia, by recruiting DNAH1 and DNALI1 in both humans and mice. These findings expand our comprehension of dynein component assembly in flagella and cilia and provide a valuable marker for genetic counseling and diagnosis of asthenoteratozoospermia in clinical practice.

genetics↗

bioRxiv2024

Pharmacological inhibition of macrophage triglyceride biosynthesis pathways does not improve Mycobacterium tuberculosis control in infected mice

Triglyceride rich macrophages (foam cells) are a hallmark of necrotic granulomas in tuberculosis, and multiple antimicrobial functions are down-regulated in these cells. In this study, we assessed the ability of two different compounds to reduce triglyceride content and intracellular burden in Mycobacterium tuberculosis (Mtb)-infected macrophages: A-922500 (DGATi), an inhibitor of diacylglycerol acyltransferase 1, an enzyme involved in triglyceride synthesis; and LY2584702 (p70S6Ki), an inhibitor of p70 S6 kinase, a serine/threonine kinase involved in mTORC-1dependent lipid biogenesis. Additionally, we evaluated the adjunctive activity of these inhibitors as host-directed therapies against chronic Mtb infection in C3HeB/FeJ mice. DGATi and p70S6Ki significantly reduced the lipid content and bacillary burden in Mtb-infected human monocyte-derived macrophages. In Mtb-infected mice, each inhibitor reduced the triglyceride content (P[≤] 0.0001) in cells from bronchoalveolar lavage samples. Adjunctive treatment of DGATi with isoniazid and p70S6Ki monotherapy reduced the lipid droplet content (P[≤] 0.05) within lung macrophages of Mtb-infected mice. However, neither inhibitor reduced the lung bacterial burden in Mtb-infected mice alone or in combination with isoniazid, and they did not alter lung inflammation. These findings provide further insights into the role of foam cells in tuberculosis pathogenesis and the utility of interventions targeting these cell populations as adjunctive host-directed therapies.

pharmacology and toxicology↗

bioRxiv2023

Exploration of the intricacies of low light-induced changes in cigar tobacco leaf anticlinal growth: A holistic approach from anatomical and hormonal levels to gene expression

Cigar tobacco stands as a pivotal economic crop, with its leaf growth and development profoundly influenced by light intensity. It specifically aims to investigate how leaf morphology and anticlinal growth respond to varying light intensities, including normal light intensity (NL-300 mol m-2 s-1) and lower light intensity (LL-100 mol m-2 s-1). The research elucidates significant morphological shifts in cigar tobacco leaves under LL, revealing notable alterations in leaf area, leaf length, and leaf width. Early reductions in leaf dimensions, ranging from 30% to 48%, were succeeded by a substantial enhancement in expansion rates from day 9 to day 26, contributing to expanded leaf surfaces at later stages. Upper epidermis thickness declined by 29-19%, with a notably slower expansion rate in the initial 20 days. Palisade cell length consistently decreased by 52-17%, corresponding with upper epidermis trends. Spongy tissue thickness was reduced by 31-12%, with a slower expansion rate in LL for the initial 14 days, and leaf thickness dropped by 34-11%. LL resulted in slower leaf anticlinal expansion, leading to reduced leaf thickness (LT). LL significantly influenced phytohormones in cigar tobacco leaves. Gibberellic acid (41% to 16%) and auxin (20% to 35%) levels were found in higher amounts, while cytokinin levels (19% to 5%) were lowered compared to NL, indicating the intricate regulatory role of light in hormonal dynamics. The observed increase in LT and different cell layers at specific time points (day 8, day 12, day 24, and day 28) under LL, although lower than NL, may be attributed to elevated expression of genes related to cell expansion, including GRF1, XTH, and SAUR19 at those time points. This comprehensive understanding elucidates the intricate mechanisms by which light intensity orchestrates the multifaceted processes governing leaf anatomy and anticlinal expansion in cigar tobacco plants.

physiology↗

bioRxiv2023

scLongTree: an accurate computational tool to infer the longitudinal tree for scDNAseq data

Longitudinal single-cell DNA sequencing (scDNA-seq) refers to single-cell data sequenced at different time points providing more knowledge of the order of mutations than scDNA-seq taken at only one time point. The technique can facilitate the inference of subclonal trees that depict the evolution of cancer cells and facilitate understanding of how cancer grows, with implications for prognosis and treatment. There is currently a scarcity of tools that can infer subclonal trees based on longitudinal scDNA-seq, and existing tools are limited in accuracy and scale. We therefore introduce scLongTree, a computational tool that can accurately infer a subclonal tree based on longitudinal scDNA-seq. ScLongTree is scalable to hundreds of mutations, and outperforms state-of-the-art tools such as LACE, SCITE, and SiCloneFit on a comprehensive simulated dataset. Tests on a real dataset, SA501, showed that scLongTree can more accurately interpret the progressive growth of the tumor than LACE, and is more robust to different numbers of mutations being used. Tests on a large AML dataset AML107, which has 4,617 cells, show that scLongTree is scalable to thousands of cells. ScLongTree is freely available on https://github.com/compbio-mallory/sc_longitudinal_infer. Key pointsO_LIWe propose scLongTree that can infer the subclonal longitudinal tree for cancer given single-cell DNA sequencing data, and thus can facilitate the study of cancer evolution given the dataset from multiple time points. C_LIO_LIMultiple simulated data show that scLongTree is more accurate than existing state-of-the-art methods such as LACE, SCITE and SiCloneFit. C_LIO_LIScLongTree has been shown to have a higher scalability than LACE and thus can be applicable to the datasets that have hundreds of mutations. C_LIO_LIThe experiment on a real SA501 shows that scLongTree is more robust to the number of mutations than LACE. It consistently generates the same longitudinal tree even under different sets of mutations. C_LIO_LIThe experiment on AML107 shows that scLongTree is scalable to thousands of cells. C_LI

bioinformatics↗