Nishanth Ulhas Nair

Bio: Nishanth Ulhas Nair is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Cancer & Medicine. The author has an hindex of 12, co-authored 39 publications receiving 427 citations. Previous affiliations of Nishanth Ulhas Nair include University of Maryland, College Park & University of Basel.

Genome-wide evaluation of histone methylation changes associated with leaf senescence in Arabidopsis.

Abstract: Leaf senescence is the orderly dismantling of older tissue that allows recycling of nutrients to developing portions of the plant and is accompanied by major changes in gene expression. Histone modifications correlate to levels of gene expression, and this study utilizes ChIP-seq to classify activating H3K4me3 and silencing H3K27me3 marks on a genome-wide scale for soil-grown mature and naturally senescent Arabidopsis leaves. ChIPnorm was used to normalize data sets and identify genomic regions with significant differences in the two histone methylation patterns, and the differences were correlated to changes in gene expression. Genes that showed an increase in the H3K4me3 mark in older leaves were senescence up-regulated, while genes that showed a decrease in the H3K4me3 mark in the older leaves were senescence down-regulated. For the H3K27me3 modification, genes that lost the H3K27me3 mark in older tissue were senescence up-regulated. Only a small number of genes gained the H3K27me3 mark, and these were senescence down-regulated. Approximately 50% of senescence up-regulated genes lacked the H3K4me3 mark in both mature and senescent leaf tissue. Two of these genes, SAG12 and At1g73220, display strong senescence up-regulation without the activating H3K4me3 histone modification. This study provides an initial epigenetic framework for the developmental transition into senescence.

System and method of using multi pattern viterbi algorithm for joint decoding of multiple patterns

Abstract: Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most- likely predecessor state and a corresponding most- likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.

Synthetic lethal combination targeting BET uncovered intrinsic susceptibility of TNBC to ferroptosis

Abstract: Identification of targeted therapies for TNBC is an urgent medical need. Using a drug combination screen reliant on synthetic lethal interactions, we identified clinically relevant combination therapies for different TNBC subtypes. Two drug combinations targeting the BET family were further explored. The first, targeting BET and CXCR2, is specific for mesenchymal TNBC and induces apoptosis, whereas the second, targeting BET and the proteasome, is effective for major TNBC subtypes and triggers ferroptosis. Ferroptosis was induced at low drug doses and was associated with increased cellular iron and decreased glutathione levels, concomitant with reduced levels of GPX4 and key glutathione biosynthesis genes. Further functional studies, analysis of clinical datasets and breast cancer specimens revealed a unique vulnerability of TNBC to ferroptosis inducers, enrichment of ferroptosis gene signature, and differential expression of key proteins that increase labile iron and decrease glutathione levels. This study identified potent combination therapies for TNBC and unveiled ferroptosis as a promising therapeutic strategy.

The SIB Swiss Institute of bioinformatics' resources: Focus on curated databases

Abstract: The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Intelligent Automated Assistant

Abstract: An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Discovery and characterization of chromatin states for systematic annotation of the human genome

Abstract: A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal chromatin states in human T cells, based on recurrent and spatially coherent combinations of chromatin marks.We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, largescale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.

The origin and evolution of cell types

Abstract: Cell types are the basic building blocks of multicellular organisms and are extensively diversified in animals. Despite recent advances in characterizing cell types, classification schemes remain ambiguous. We propose an evolutionary definition of a cell type that allows cell types to be delineated and compared within and between species. Key to cell type identity are evolutionary changes in the 'core regulatory complex' (CoRC) of transcription factors, that make emergent sister cell types distinct, enable their independent evolution and regulate cell type-specific traits termed apomeres. We discuss the distinction between developmental and evolutionary lineages, and present a roadmap for future research.