Biology subjects

Raghu, A.

Publications and source records attributed to Raghu, A..

2 recordsLinked to original sources

bioRxiv2024

Selective Targeting of a Defined Subpopulation of Corticospinal Neurons using a Novel Klhl14-Cre Mouse Line Enables Molecular and Anatomical Investigations through Development into Maturity

The corticospinal tract (CST) facilitates skilled, precise movements, which necessitates that subcerebral projection neurons (SCPN) establish segmentally specific connectivity with brainstem and spinal circuits. Developmental molecular delineation enables prospective identification of corticospinal neurons (CSN) projecting to thoraco-lumbar spinal segments; however, it remains unclear whether other SCPN subpopulations in developing sensorimotor cortex can be prospectively identified in this manner. Such molecular tools could enable investigations of SCPN circuitry with precision and specificity. During development, Kelch-like 14 (Klhl14) is specifically expressed by a specific SCPN subpopulation, CSNBC-lat, that reside in lateral sensorimotor cortex with axonal projections exclusively to bulbar-cervical targets. In this study, we generated Klhl14-T2A-Cre knock-in mice to investigate SCPN that are Klhl14+ during development into maturity. Using conditional anterograde and retrograde labeling, we find that Klhl14-Cre is specifically expressed by CSNBC-lat only at specific developmental time points. We establish conditional viral labeling in Klhl14-T2A-Cre mice as a new approach to reliably investigate CSNBC-lat axon targeting and confirm that this identifies known molecular regulators of CSN axon targeting. Therefore, Klhl14-T2A-Cre mice can be used as a novel tool for identifying molecular regulators of CST axon guidance in a relatively high-throughput manner in vivo. Finally, we demonstrate that intersectional viral labeling enables precise targeting of only Klhl14-Cre+ CSNBC-lat in the adult central nervous system. Together, our results establish that developmental molecular delineation of SCPN subpopulations can be used to selectively and specifically investigate their development, as well as anatomical and functional organization into maturity. Significance StatementThe cortex connects to brainstem and spinal targets through subcerebral projection neurons (SCPN), which exhibit molecular diversity during development based on their neocortical location and axonal targets. We generated a novel Klhl14-Cre mouse line to utilize this developmental delineation and drive Cre expression in a specific SCPN subpopulation. This developmental specificity enabled investigation of 1) areal locations of Klhl14+ SCPN in mature cortex, 2) their axonal collateralization at maturity, and 3) which genes can control their axon targeting. Using intersectional tools, we can also selectively label these neurons in the adult CNS. Therefore, developmental molecular delineation of SCPN not only provides prospective identification but also enables molecular analysis during development, as well as anatomical and functional investigations in adulthood.

neuroscience↗

bioRxiv2024

Deep Learning Based Identification of Tissue of Origin for Carcinomas of Unknown Primary utilizing micro-RNA expression

Carcinoma of Unknown Primary (CUP) is a subset of metastatic cancers in which the primary tissue source, or origin, remains unidentified. CUP accounts for three to five percent of all malignancies [2]. Representing an exceptionally aggressive category of metastatic cancers, the median survival of those diagnosed with CUP is approximately three to six months [1]. The tissue in which a cancer arises plays a key role in our understanding of altered gene expression, altered cellular pathways, and sensitivities to various forms of cell death in cancer cells [3]. Thus, the lack of knowledge on tissue of origin makes it difficult to devise tailored treatments for patients with CUP [4]. Developing clinically implementable methods to identify the tissue of origin of the primary site is crucial in treating CUP patients [4]. In particular, the expression profiles of non-coding RNAs can provide insight into the tissue of origin for CUP. Non-coding RNAs provide a robust route to clinical implementation due to their resistance against chemical degradation [5]. In this work, we investigate the potential of microRNAs as highly accurate biomarkers for detecting the tissue of origin for metastatic cancers. We further hypothesize that data driven approaches can identify specific microRNA biomarker targets. We used microRNA expression data from the Cancer Genome Atlas (TCGA) dataset [6] and assessed various machine learning approaches. Our results show that it is possible to design robust classifiers to detect the tissue of origin for metastatic samples on the TCGA dataset with an accuracy of up to 96%, which may be utilized in situations of CUP. As a validation of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive (SRA) [7]. Our findings demonstrate that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% with decision trees to 93.2% with logistic regression, finally achieving 96.1% accuracy using deep learning on metastatic samples. On the SRA validation set, a lower accuracy of 41.2% was achieved by decision tree, while deep learning achieved a higher accuracy of 81.2%. Notably, our feature importance analysis showed the top three important biomarkers for predicting tissue of origin to be mir-10b, mir-205, and mir-196b, which aligns with previous work [10]. Our findings highlight the potential of using machine learning techniques to devise tests for detecting tissue of origin for CUP. Since microRNAs are carried throughout the body via vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma [11]. Our work serves as a foundation towards developing blood-based cancer detection tests based on microRNA presence.

cancer biology↗