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Ramanathan, L.

Publications and source records attributed to Ramanathan, L..

3 recordsLinked to original sources

Large-scale Perturbation of Systems Biology-Derived Genes Reveals Modifiers of HD-associated Transcriptomic Networks and Pathology

In Huntingtons disease (HD), disease-driver genes are broadly expressed, but pathogenic players specific to vulnerable neurons remain poorly defined. Leveraging a previously defined mutant huntingtin (mHtt) CAG-length-associated gene network, we perturbed 115 module hub genes with heterozygous knockout (KO-het) to assess their striatal transcriptomic modifier effects in wildtype and Q140 HD mice. We generated 3,592 striatal RNA-seq datasets, mapped 6,517 perturbagen-responder gene pairs, and uncovered regulators of medium spiny neuron (MSN) identity gene expression and DNA- methylase/demethylase-sensitive genes. We developed a bioinformatic pipeline to rank the perturbations with significant impacts on striatal transcriptome and HD-associated gene networks in wildtype or Q140 mice. KO-het for FoxP1 and Scn4b (two MSN-selective genes) exacerbated, whereas Pdp1 KO-het ameliorated, Q140 striatal pathology. Importantly, knockdown of functionally opposing ion channels, SCN4B and KCNH4, dichotomously affected aggregation and neurodegeneration in reprogrammed HD patient MSNs. Together, our study rigorously evaluated systems biology-derived candidates to identify modifiers of HD-associated molecular networks and pathology, providing an in vivo perturbation- transcriptome resource and highlighting genes involved in MSN excitability, transcription, calcium signaling, and mitochondrial metabolism in HD pathogenesis.

genetics↗

Msh3 and Pms1 Set Neuronal CAG-repeat Migration Rate to Drive Selective Striatal and Cortical Pathogenesis in HD Mice

Modifiers of Huntingtons disease (HD) include mismatch repair (MMR) genes; however, their underlying disease-altering mechanisms remain unresolved. Knockout (KO) alleles for 9 HD GWAS modifiers/MMR genes were crossed to the Q140 Huntingtin (mHtt) knock-in mice to probe such mechanisms. Four KO mice strongly (Msh3 and Pms1) or moderately (Msh2 and Mlh1) rescue a triad of adult-onset, striatal medium-spiny-neuron (MSN)-selective phenotypes: somatic Htt DNA CAG-repeat expansion, transcriptionopathy, and mHtt protein aggregation. Comparatively, Q140 cortex also exhibits an analogous, but later-onset, pathogenic triad that is Msh3-dependent. Remarkably, Q140/homozygous Msh3-KO lacks visible mHtt aggregates in the brain, even at advanced ages (20-months). Moreover, Msh3-deficiency prevents striatal synaptic marker loss, astrogliosis, and locomotor impairment in HD mice. Purified Q140 MSN nuclei exhibit highly linear age-dependent mHtt DNA repeat expansion (i.e. repeat migration), with modal-CAG increasing at +8.8 repeats/month (R2=0.98). This linear rate is reduced to 2.3 and 0.3 repeats/month in Q140 with Msh3 heterozygous and homozygous alleles, respectively. Our study defines somatic Htt CAG-repeat thresholds below which there are no detectable mHtt nuclear or neuropil aggregates. Mild transcriptionopathy can still occur in Q140 mice with stabilized Htt 140-CAG repeats, but the majority of transcriptomic changes are due to somatic repeat expansion. Our analysis reveals 479 genes with expression levels highly correlated with modal-CAG length in MSNs. Thus, our study mechanistically connects HD GWAS genes to selective neuronal vulnerability in HD, in which Msh3 and Pms1 set the linear rate of neuronal mHtt CAG-repeat migration to drive repeat-length dependent pathogenesis; and provides a preclinical platform for targeting these genes for HD suppression across brain regions. One Sentence SummaryMsh3 and Pms1 are genetic drivers of sequential striatal and cortical pathogenesis in Q140 mice by mediating selective CAG-repeat migration in HD vulnerable neurons.

neuroscience↗

Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Conventional molecular recognition elements, such as antibodies, present issues for the development of biomolecular assays for use in point-of-care devices, implantable/wearables, and under-resourced settings. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, which are often diagnosed at advanced stages, leading to low survival rates. We investigated the platform for detection in uterine lavage samples, which are enriched with cancer biomarkers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from cancer patients. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

bioengineering↗