Hari Krishna Yalamanchili

Bio: Hari Krishna Yalamanchili is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 16, co-authored 37 publications receiving 636 citations. Previous affiliations of Hari Krishna Yalamanchili include Boston Children's Hospital & Li Ka Shing Faculty of Medicine, University of Hong Kong.

Extensive cryptic splicing upon loss of RBM17 and TDP43 in neurodegeneration models

Abstract: Splicing regulation is an important step of post-transcriptional gene regulation. It is a highly dynamic process orchestrated by RNA-binding proteins (RBPs). RBP dysfunction and global splicing dysregulation have been implicated in many human diseases, but the in vivo functions of most RBPs and the splicing outcome upon their loss remain largely unexplored. Here we report that constitutive deletion of Rbm17, which encodes an RBP with a putative role in splicing, causes early embryonic lethality in mice and that its loss in Purkinje neurons leads to rapid degeneration. Transcriptome profiling of Rbm17-deficient and control neurons and subsequent splicing analyses using CrypSplice, a new computational method that we developed, revealed that more than half of RBM17-dependent splicing changes are cryptic. Importantly, RBM17 represses cryptic splicing of genes that likely contribute to motor coordination and cell survival. This finding prompted us to re-analyze published datasets from a recent report on TDP-43, an RBP implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), as it was demonstrated that TDP-43 represses cryptic exon splicing to promote cell survival. We uncovered a large number of TDP-43-dependent splicing defects that were not previously discovered, revealing that TDP-43 extensively regulates cryptic splicing. Moreover, we found a significant overlap in genes that undergo both RBM17- and TDP-43-dependent cryptic splicing repression, many of which are associated with survival. We propose that repression of cryptic splicing by RBPs is critical for neuronal health and survival. CrypSplice is available at www.liuzlab.org/CrypSplice.

Transcriptional Regulation by ATOH1 and its Target SPDEF in the Intestine

Abstract: Background & Aims The transcription factor atonal homolog 1 (ATOH1) controls the fate of intestinal progenitors downstream of the Notch signaling pathway. Intestinal progenitors that escape Notch activation express high levels of ATOH1 and commit to a secretory lineage fate, implicating ATOH1 as a gatekeeper for differentiation of intestinal epithelial cells. Although some transcription factors downstream of ATOH1, such as SPDEF, have been identified to specify differentiation and maturation of specific cell types, the bona fide transcriptional targets of ATOH1 still largely are unknown. Here, we aimed to identify ATOH1 targets and to identify transcription factors that are likely to co-regulate gene expression with ATOH1. Methods We used a combination of chromatin immunoprecipitation and messenger RNA–based high-throughput sequencing (ChIP-seq and RNA-seq), together with cell sorting and transgenic mice, to identify direct targets of ATOH1, and establish the epistatic relationship between ATOH1 and SPDEF. Results By using unbiased genome-wide approaches, we identified more than 700 genes as ATOH1 transcriptional targets in adult small intestine and colon. Ontology analysis indicated that ATOH1 directly regulates genes involved in specification and function of secretory cells. De novo motif analysis of ATOH1 targets identified SPDEF as a putative transcriptional co-regulator of ATOH1. Functional epistasis experiments in transgenic mice show that SPDEF amplifies ATOH1-dependent transcription but cannot independently initiate transcription of ATOH1 target genes. Conclusions This study unveils the direct targets of ATOH1 in the adult intestines and illuminates the transcriptional events that initiate the specification and function of intestinal secretory lineages.

An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data.

Abstract: Transposable elements (TEs) are DNA sequences which are capable of moving from one location to another and represent a large proportion (45%) of the human genome. TEs have functional roles in a variety of biological phenomena such as cancer, neurodegenerative disease, and aging. Rapid development in RNA-sequencing technology has enabled us, for the first time, to study the activity of TE at the systems level.However, efficient TE analysis tools are not yet developed. In this work, we developed SalmonTE, a fast and reliable pipeline for the quantification of TEs from RNA-seq data. We benchmarked our tool against TEtranscripts, a widely used TE quantification method, and three other quantification methods using several RNA-seq datasets from Drosophila melanogaster and human cell-line. We achieved 20 times faster execution speed without compromising the accuracy. This pipeline will enable the biomedical research community to quantify and analyze TEs from large amounts of data and lead to novel TE centric discoveries.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

Conflict of interest statement. None declared.

Abstract: Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS

Flows in Networks

Abstract: In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

Gene co-expression analysis for functional classification and gene-disease predictions

Abstract: Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.