Biology subjects

Owens, M.

Publications and source records attributed to Owens, M..

7 recordsLinked to original sources

bioRxiv2026

The Interaction Between Miro and TRAK is not Required for Bulk Mitochondrial Trafficking

In metazoans, mitochondria optimally distribute to sites of need through long-range transport events on microtubules. The prevailing model for this trafficking mechanism is that the tail-anchored calcium-binding GTPase, Miro, recruits cytosolic TRAK and associated molecular motors to the outer mitochondrial membrane. Therefore, Miro is proposed to be an obligate adaptor for TRAK required for bulk mitochondrial transport, a process that is considered particularly important for long-range trafficking in neurons, and thus, for viability. Here, we impaired Miro-TRAK interaction in vivo by introducing a point mutation into the Drosophila TRAK orthologue Milton, that impairs its interaction with Miro, based on recent structural evidence. Flies harbouring this point mutation are viable to adulthood. Moreover, neurons carrying this mutation exhibit little to no observable reduction in axonal mitochondria. Mutant flies, however, display progressive loss of motor function with age and reduced lifespan. We therefore call into question the long-standing view that Miro plays an obligatory role in mitochondrial trafficking and challenge the canonical model for mitochondrial transport.

cell biology↗

bioRxiv2025

Microtubule curling as an efficient readout to uncover fundamental concepts of axonal cell biology

Nerve fibres (aka axons) are the slender, up-to-meter-long processes of nerve cells that wire nervous systems. These delicate structures must survive for an organisms lifetime, making them prime lesion sites in neurodegeneration. Their long-term maintenance requires the homeostasis of complex local cell biology upheld by motor protein-driven transport along microtubule bundles that run uninterrupted along axons. To gain an understanding of axonal homeostasis, we report and discuss here the functional loss of 105 genes from a wide range of cell biological processes using a standardised Drosophila primary neuron system. [~]40% of these gene deficiencies caused microtubule bundle disturbances referred to as microtubule-curling, which we use as an indicator of axonal atrophy. Our live imaging showed that microtubule-curling initiates in areas where axons widen, such as areas close to the soma, branch points or growth cones. In wild-type neurons, any initiated curling was contained, but it persisted as growing footprints in mutant curl-promoting conditions. Closer analyses of 20 curl-promoting conditions suggested a general classification into two groups: mutations affecting axonal physiology cause ROS-mediated microtubule-curling, whereas mutations causing motor hyperactivation or affecting microtubule-regulating proteins cause structurally induced ROS-independent curling. As will be discussed, our data provide consistent support for the previously proposed dependency cycle of local axon homeostasis model which can explain the long-standing conundrum that genes from a wide range of cell biological processes often have mutational links to the same class of inherited neurodegenerative disease.

cell biology↗

bioRxiv2025

Assessing impacts of mitochondrial dysfunction on axonal microtubule bundles as potential mechanism in neurodegeneration

Mitochondrial dysfunction is an important cause for neurodegeneration, often associated with dyshomeostasis of reactive oxygen species, i.e. oxidative stress. However, apart from ATP production, mitochondria have many other functions the aberration of which may impact neurons in very different ways. Oxidative stress can cause the deterioration of axonal microtubule bundles, thus critically affecting the highways for life-sustaining transport and providing a potential path to neurodegeneration. We recently found that aberrant transport of mitochondria can have this effect by causing oxidative stress. We therefore asked which aberrations of mitochondrial physiology might impact microtubules, which of these might explain the observed consequences of aberrant mitochondrial transport, and whether mitochondria-induced microtubule phenotypes are always mediated by oxidative stress. Using one consistent Drosophila primary neuron system, we deleted 13 different mitochondrial factors known to be detrimental for neurons in vivo. Losses of five factors caused MT damage, all involving oxidative stress, hence supporting the path from mitochondria via oxidative stress to microtubule deterioration; we discuss Sod2 as potential candidate explaining effects of mitochondrial transport aberration. However, the loss of eight factors - seven of them important mitochondrial morphogenesis regulators - caused no microtubule damage, suggesting potential oxidative stress-independent pathways. Summary StatementAssessing mutant effects of 13 mitochondrial factors on axonal microtubule organisation to unravel potential mechanisms underpinning neurodegeneration

cell biology↗

bioRxiv2025

Deep learning using structural MRI massively improves prediction accuracy of body mass index

Obesity is a major public health problem globally and there is considerable interest in the neural mechanisms in food overconsumption. Artificial intelligence (AI), particularly machine learning, has shown promise in characterizing links between brain morphometry and obesity. In 1106 adults, compared to other forms of machine learning, deep learning using 3D convolutional neural networks (3D-CNN) dramatically improves prediction of body mass index (BMI). The 3D-CNN model robustly predicted BMI (R2 =.325), outperforming random forest, elastic net, and tabnet models (R2s<.07) in a lockbox sample. Explainable AI analyses revealed the specific brain regions implicated and these regions were moderately associated with delay discounting, fluid cognition, gait speed, dexterity, and alcohol use. Collectively, these findings reveal the value of deep learning for understanding of the neural basis and motivational processes in the neurobiology of obesity.

neuroscience↗

bioRxiv2025

Different mechanisms link gain and loss of kinesin functions to axonal degeneration

Axons are the slender, often meter-long projections of neurons that form the biological cables wiring our bodies. Most of these delicate structures must survive for an organisms lifetime, meaning up to a century in humans. Long-term maintenance and sustained functionality of axons requires motor protein-driven transport distributing life-sustaining materials and organelles to places of need. It seems therefore plausible that loss of motor function would cause axon degeneration; however, also gain-of-function conditions were linked to disorders including motor neuron disease or spastic paraplegia. To understand this phenomenon, we studied [~]40 genetic manipulations of motor proteins, cargo linkers and regulators of reactive oxygen species in one standardised Drosophila primary neuron system. Using axonal microtubule bundle organisation as a relevant readout reflecting the state of axon integrity, we found that losses of Dynein heavy chain, KIF1A/Unc-104 and KIF5/Kinesin heavy chain (Khc) all cause bundle disintegration in the form of chaotically curled microtubules. Detailed functional studies of Khc and its adaptor proteins revealed that losses of mitochondrial or lysosomal transport cause ROS dyshomeostasis, which is a microtubule-curl-inducing condition in fly and mouse neurons alike. We find that hyper-activated Khc induces the same microtubule curling phenotype, not through ROS but likely more directly through enhanced mechanical forces. Studies with loss of Unc-104 or KIFBP and expression of an ALS-linked mutant form of the human Khc orthologue KIF5A suggest that loss or hyperactivation of different types of transport motors cause MT curling as a shared feature. We discuss a model which can explain our findings and their relevance for understanding motor-linked neurodegeneration.

neuroscience↗

bioRxiv2023

Sexually dimorphic RNA helicases DDX3X and DDX3Y differentially regulate hollow condensates of DDX3X disease variants

Mutations in the RNA helicase DDX3X, implicated in various cancers and neurodevelopmental disorders, often impair RNA unwinding and translation. However, the mechanisms underlying this impairment and the differential interactions of DDX3X mutants with wild-type (WT) X-linked DDX3X and Y-linked homolog DDX3Y remain elusive. This study reveals that specific DDX3X mutants more frequently found in disease form distinct hollow condensates in cells. Using a combined structural, biochemical, and single-molecule microscopy study, we show that reduced ATPase and RNA release activities contribute to condensate formation and the catalytic deficits result from inhibiting the catalytic cycle at multiple steps. Proteomic investigations further demonstrate that these hollow condensates sequester WT DDX3X/DDX3Y and other proteins crucial for diverse signaling pathways. WT DDX3X enhances the dynamics of heterogeneous mutant/WT hollow condensates more effectively than DDX3Y. These findings offer valuable insights into the catalytic defects of specific DDX3X mutants and their differential interactions with wild-type DDX3X and DDX3Y, potentially explaining sex biases in disease.

molecular biology↗

bioRxiv2021

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning

IntroductionMultidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. MethodsWe present Translational Genomics platform for Tuberculosis (GenTB), a web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTBs predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. ResultsAll four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs was 77.6% (95% CI 76.6 - 78.5%) and 75.4% (95% CI 74.5 - 76.4%) respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4 - 75.3%) and Mykrobe at 71.9% (95% CI 70.9 - 72.9%). The higher sensitivities were at an expense of [≤]1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5 - 97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (<10x across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants ConclusionGenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu, and the source code is available at https://github.com/farhat-lab/gentb-site.

bioinformatics↗