Biology subjects

Raghavan, M.

Publications and source records attributed to Raghavan, M..

4 recordsLinked to original sources

bioRxiv2021

Neuro-musculoskeletal Upper Limb in-silico asvirtual patient

Virtual patients and physiologies allow experimentation, design, and early-stage clinical trials in-silico. Virtual patient technology for human movement systems that encompasses musculoskeleton and its neural control are few and far in between. In this work, we present one such neuro-musculoskeletal upper limb in-silico model. This upper limb is both modular in architecture and generates movement as an emergent phenomenon out of a multiscale co-simulation of spinal cord neural control and musculoskeletal dynamics. It is developed on the NEUROiD movement simulation platform that enables a co-simulation of popular neural simulator NEURON and the musculoskeletal simulator OpenSim. In this work, we describe the design and development of the upper limb in a modular fashion, while reusing existing models and modules. We further characterize and demonstrate the use of this model in generating a range of commonly observed movements by means of a spatio temporal stimulation pattern delivered to the cervical spinal cord. We believe this work enables a first and small step towards an in-silico paradigms for understanding upper limb movement, disease pathology, medication, and rehabilitation.

neuroscience↗

bioRxiv2021

A NOVEL PARADIGM FOR DEEP REINFORCEMENT LEARNING OF BIOMIMETIC SYSTEMS

Mechanisms behind neural control of movement have been an active area of research. Goal-directed movement is a common experimental paradigm used to understand these mechanisms and relevant neural pathways. In this paper, we attempt to build an anatomically and physiologically realistic model of spinal cord along with the relevant circuitry and interface it with a musculoskeletal model of an upper limb, using the NEUROiD platform. The neuronal model (simulated on NEURON) and the musculoskeletal model (simulated on OpenSim) are cosimulated on NEUROiD. We then use Deep Reinforcement Learning to obtain a functionally equivalent model of the supraspinal components and the descending cortical activations feeding into the last-order interneurons and motoneurons. Uniplanar goal directed movement of the elbow joint was used as the goal for the learning algorithm. Key aspects of our work are: (1) Our solution converges naturally to the triphasic response observed in goal directed tasks (2) Gradually increasing the complexity of task helped in faster learning (3) In response to corticospinal inputs, our model could produce movements on which it was not explicitly trained, but were close to the trained movements. Being able to generate movements on which the model was not explicitly trained, implies that the movement repertoire that a biomimetic model needs to learn, could be much smaller than the complete set of movements it can execute. We hope that this will lead to building larger and complex biomimetic systems, one block at a time.

bioengineering↗

bioRxiv2021

Crosstalk between AML and stromal cells triggers acetate secretion through the metabolic rewiring of stromal cells

Acute myeloid leukaemia (AML) cells interact and modulate components of their surrounding microenvironment into their own benefit. Stromal cells have been shown to support AML survival and progression through various mechanisms. Nonetheless, it is unclear whether AML cells could establish beneficial metabolic interactions with stromal cells. Here, we identify a novel metabolic crosstalk between AML and stromal cells where AML cells prompt stromal cells to secrete acetate for their own consumption to feed the tricarboxylic acid cycle (TCA). By performing transcriptome analysis, tracer-based metabolic NMR analysis and ROS measurements, we observe that stromal cells present a higher rate of glycolysis. We also find that acetate in stromal cells is derived from pyruvate via chemical conversion under the influence of reactive oxygen species (ROS) following ROS transfer from AML to stromal cells via gap junctions. Overall, we present a unique metabolic communication between AML and stromal cells that could potentially be exploited for adjuvant therapy.

cell biology↗

bioRxiv2020

Mpl is activated by dimers of MPN-linked calreticulin mutants stabilized by disulfide bonds and ionic interactions

Myeloproliferative neoplasms (MPNs) are frequently driven by insertions and deletions within the gene encoding calreticulin (CRT). CRTDel52 and CRTIns5 are recurrent mutations. Although oncogenic transformation requires both mutated CRT and the myeloproliferative leukemia protein (Mpl), the molecular mechanism of CRT-mediated constitutive activation of Mpl is unknown. Our studies reveal that the novel C-domain of CRTDel52 encodes specificity both for Mpl binding and for disulfide-mediated CRT dimerization. Disulfide-stabilized CRTDel52 dimers and multimers are observed in MPN patient-derived platelet lysates and in transfected mammalian cells. Cysteine mutations within both the novel C-domain (C400A and C404A) and the conserved N-domain (C163A) of CRTDel52 are required to reduce disulfide-mediated dimers and multimers of CRTDel52. Based on these data and published structures of crystalized CRT oligomers, we tested the relevance of ionic interactions between charged residues proximal to C163 at the N-domain dimerization interface. Charge alteration at these residues affected dimerization and multimerization of both wild type and CRTDel52. Elimination of intermolecular disulfides and disruption of ionic interactions at both proposed dimerization interfaces was required to abrogate the ability of CRTDel52 to induce cytokine-independent cell proliferation via Mpl. Based on these findings, we propose a structural model of the Mpl-activating CRTDel52 unit as a covalently-linked dimer that is stabilized by disulfides and ionic interactions at both the C-domain and N-domain. MPNs exploit a natural dimerization interface of CRT combined with C-domain gain-of-functions to achieve cell transformation.

cancer biology↗