Biology subjects

Malik, A. N.

Publications and source records attributed to Malik, A. N..

5 recordsLinked to original sources

bioRxiv2026

Why diabetes matters in dementia studies. Excluding diabetes status masks regional mitochondrial DNA copy number changes in human hippocampus, amygdala, and cerebellum in Alzheimers disease

STRUCTURED ABSTRACTO_ST_ABSINTRODUCTIONC_ST_ABSDiabetes is a major risk factor for Alzheimers disease (AD), and both diseases involve mitochondrial dysfunction. We hypothesised that AD is associated with reduced mitochondrial DNA copy number (mtDNA-CN) in vulnerable brain regions, and that diabetes modifies these changes. METHODSPost-mortem hippocampus, amygdala, and cerebellum samples (N=66-77) from non-cognitively impaired (NCI) and AD donors, with and without diabetes, were analysed. mtDNA-CN was quantified by absolute quantification. RESULTSOverall, mtDNA-CN was lower in AD. However, stratification by diabetes revealed opposite changes: non-diabetic AD cases showed reduced mtDNA-CN, whereas diabetic cases showed higher mtDNA-CN across all regions irrespective of cognitive status. DISCUSSIONThese findings confirm multiregional loss of mtDNA-CN in the AD brain, most evident in the absence of diabetes. The functional significance of higher mtDNA-CN in the diabetic brain remains unclear, but evidence that diabetes can mask effects has important implications for dementia studies.

neuroscience↗

bioRxiv2026

Accurate quantification of canine mitochondrial DNA copy number and its evaluation as a biomarker of brain injury

Acute brain injury is challenging to manage in veterinary medicine, with limited validated means of prognostication currently available. The brain is rich in mitochondrial content and contains thousands of copies of mitochondrial DNA (mtDNA) per cell. We hypothesized that brain cell loss following acute brain injury may result in release of mtDNA into the systemic circulation. To investigate this, mtDNA-CN was measured in blood (n=4-6/group) and cerebral cortex (n=1/group) samples from dogs with and without brain injury using absolute quantification by real-time qPCR. By filtering out regions with homology to nuclear mitochondrial insertion sequences (NumtS) and repetitive regions, oligonucleotide primers were designed to the canine mitochondrial and nuclear genomes. In controls, blood mtDNA-CN ranged from 98 to 288 (mean 193{+/-}72), in cases of brain injury, there was a non-significant trend for higher mtDNA-CN, ranging from 163-453 (mean 244{+/-}106). As expected, cerebral cortex contained higher mtDNA-CN than the blood. In a single case with serial sampling, mtDNA-CN increased five days post-injury. We present for the first time an assay to accurately quantify mtDNA-CN in canine samples, with potential as a biomarker for acute brain injury in veterinary practice. Simple SummaryThis work describes a novel assay to accurately measure absolute levels of canine mitochondrial DNA copy number (mtDNA-CN) in biological samples. We describe the range of mtDNA-CN in canine blood and show pilot data suggesting that blood mtDNA-CN should be evaluated as a potential biomarker of acute brain injury.

molecular biology↗

bioRxiv2023

Multi-timescale reinforcement learning in the brain

To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behavior can be learned through reinforcement learning1, a class of algorithms that has been successful at training artificial agents2-6 and at characterizing the firing of dopamine neurons in the midbrain7-9. In classical reinforcement learning, agents discount future rewards exponentially according to a single time scale, controlled by the discount factor. Here, we explore the presence of multiple timescales in biological reinforcement learning. We first show that reinforcement agents learning at a multitude of timescales possess distinct computational benefits. Next, we report that dopamine neurons in mice performing two behavioral tasks encode reward prediction error with a diversity of discount time constants. Our model explains the heterogeneity of temporal discounting in both cue-evoked transient responses and slower timescale fluctuations known as dopamine ramps. Crucially, the measured discount factor of individual neurons is correlated across the two tasks suggesting that it is a cell-specific property. Together, our results provide a new paradigm to understand functional heterogeneity in dopamine neurons, a mechanistic basis for the empirical observation that humans and animals use non-exponential discounts in many situations 10-14, and open new avenues for the design of more efficient reinforcement learning algorithms.

neuroscience↗

bioRxiv2019

A unified framework for dopamine signals across timescales

Rapid phasic activity of midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. Recent studies describing slowly increasing dopamine signals have instead proposed that they represent state values and arise independently from somatic spiking activity. Here, we developed novel experimental paradigms using virtual reality that disambiguate RPEs from values. We examined the dopamine circuit activity at various stages including somatic spiking, axonal calcium signals, and striatal dopamine concentrations. Our results demonstrate that ramping dopamine signals are consistent with RPEs rather than value, and this ramping is observed at all the stages examined. We further show that ramping dopamine signals can be driven by a dynamic stimulus that indicates a gradual approach to a reward. We provide a unified computational understanding of rapid phasic and slowly ramping dopamine signals: dopamine neurons perform a derivative-like computation over values on a moment-by-moment basis.

neuroscience↗

bioRxiv2018

Reduced mitochondrial lipid oxidation leads to fat accumulation in myosteatosis

Myosteatosis is the pathological accumulation of lipid that can occur in conjunction with atrophy and fibrosis following skeletal muscle injury. Little is known about the mechanisms by which lipid accumulates in myosteatosis, but many clinical studies have demonstrated the degree of lipid infiltration negatively correlates with muscle function and regeneration. Our objective was to determine the pathological changes that result in lipid accumulation in injured muscle fibers. We used a rat model of rotator cuff injury in this study, as the rotator cuff muscle group is particularly prone to the development of myosteatosis after injury. Muscles were collected from uninjured controls, or 10, 30, or 60 days after injury, and analyzed using a combination of muscle fiber contractility assessments, RNA sequencing, and undirected metabolomics, lipidomics and proteomics, along with bioinformatics techniques, to identify potential pathways and cellular processes that are dysregulated after rotator cuff tear. Bioinformatics analyses indicated that mitochondrial function was likely disrupted after injury. Based on these findings, and given the role that mitochondria play in lipid metabolism, we then performed targeted biochemical and imaging studies and determined that mitochondrial dysfunction and reduced fatty acid oxidation likely leads to the accumulation of lipid in myosteatosis.

physiology↗