Single-cell-led drug repurposing for Alzheimer's disease

: Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy

In search of the right literature search engine(s)

*Background*
Collecting scientific publications related to a specific topic is crucial for different phases of research, health care and ‘effective text mining’. Available bio-literature search engines vary in their ability to scan different sections of articles, for the user-provided search terms and/or phrases. Since a thorough scientific analysis of all major bibliographic tools has not been done, their selection has often remained subjective. We have considered most of the existing bio-literature search engines (http://www.shodhaka.com/startbioinfo/LitSearch.html) and performed an extensive analysis of 18 literature search engines, over a period of about 3 years. Eight different topics were taken and about 50 searches were performed using the selected search engines. The relevance of retrieved citations was carefully assessed after every search, to estimate the citation retrieval efficiency. Different other features of the search tools were also compared using a semi-quantitative method.
*Results*
The study provides the first tangible comparative account of relative retrieval efficiency, input and output features, resource coverage and a few other utilities of the bio-literature search tools. The results show that using a single search tool can lead to loss of up to 75% relevant citations in some cases. Hence, use of multiple search tools is recommended. But, it would also not be practical to use all or too many search engines. The detailed observations made in the study can assist researchers and health professionals in making a more objective selection among the search engines. A corollary study revealed relative advantages and disadvantages of the full-text scanning tools.
*Conclusion*
While many studies have attempted to compare literature search engines, important questions remained unanswered till date. Following are some of those questions, along with answers provided by the current study:
a)	Which tools should be used to get the maximum number of relevant citations with a reasonable effort? ANSWER: _Using PubMed, Scopus, Google Scholar and HighWire Press individually, and then compiling the hits into a union list is the best option. Citation-Compiler (http://www.shodhaka.com/compiler) can help to compile the results from each of the recommended tool._
b)	What is the approximate percentage of relevant citations expected to be lost if only one search engine is used? ANSWER: _About 39% of the total relevant citations were lost in searches across 4 topics; 49% hits were lost while using PubMed or HighWire Press, while 37% and 20% loss was noticed while using Google Scholar and Scopus, respectively._ 
c)	Which full text search engines can be recommended in general? ANSWER: _HighWire Press and Google Scholar._
d)	Among the mostly used search engines, which one can be recommended for best precision? ANSWER: _EBIMed._
e)	Among the mostly used search engines, which one can be recommended for best recall? ANSWER: _Depending on the type of query used, best recall could be obtained by HighWire Press or Scopus.

A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database

<p>Abstract</p> <p>Background</p> <p>In the recent years, there has been a rise in gene expression profiling reports. Unfortunately, it has not been possible to make maximum use of available gene expression data. Many databases and programs can be used to derive the possible expression patterns of mammalian genes, based on existing data. However, these available resources have limitations. For example, it is not possible to obtain a list of genes that are expressed in certain conditions. To overcome such limitations, we have taken up a new strategy to predict gene expression patterns using available information, for one tissue at a time.</p> <p>Results</p> <p>The first step of this approach involved manual collection of maximum data derived from large-scale (genome-wide) gene expression studies, pertaining to mammalian testis. These data have been compiled into a Mammalian Gene Expression Testis-database (MGEx-Tdb). This process resulted in a richer collection of gene expression data compared to other databases/resources, for multiple testicular conditions. The gene-lists collected this way in turn were exploited to derive a 'consensus' expression status for each gene, across studies. The expression information obtained from the newly developed database mostly agreed with results from multiple small-scale studies on selected genes. A comparative analysis showed that MGEx-Tdb can retrieve the gene expression information more efficiently than other commonly used databases. It has the ability to provide a clear expression status (transcribed or dormant) for most genes, in the testis tissue, under several specific physiological/experimental conditions and/or cell-types.</p> <p>Conclusions</p> <p>Manual compilation of gene expression data, which can be a painstaking process, followed by a consensus expression status determination for specific locations and conditions, can be a reliable way of making use of the existing data to predict gene expression patterns. MGEx-Tdb provides expression information for 14 different combinations of specific locations and conditions in humans (25,158 genes), 79 in mice (22,919 genes) and 23 in rats (14,108 genes). It is also the first system that can predict expression of genes with a 'reliability-score', which is calculated based on the extent of agreements and contradictions across gene-sets/studies. This new platform is publicly available at the following web address: <url>http://resource.ibab.ac.in/MGEx-Tdb/</url></p

Tumorigenesis by Meis1 overexpression is accompanied by a change of DNA target-sequence specificity which allows binding to the AP-1 element

Meis1 overexpression induces tumorigenicity but its activity is inhibited by Prep1 tumor suppressor. Why does overexpression of Meis1 cause cancer and how does Prep1 inhibit? Tumor profiling and ChIP-sequencing data in a genetically-defined set of cell lines show that: 1) The number of Meis1 and Prep1 DNA binding sites increases linearly with their concentration resulting in a strong increase of “extra” target genes. 2) At high concentration, Meis1 DNA target specificity changes such that the most enriched consensus becomes that of the AP-1 regulatory element, whereas the specific OCTA consensus is not enriched because diluted within the many extra binding sites. 3) Prep1 inhibits Meis1 tumorigenesis preventing the binding to many of the “extra” genes containing AP-1 sites. 4) The overexpression of Prep1, but not of Meis1, changes the functional genomic distribution of the binding sites, increasing seven fold the number of its “enhancer” and decreasing its “promoter” targets. 5) A specific Meis1 “oncogenic” and Prep1 “tumor suppressing” signature has been identified selecting from the pool of genes bound by each protein those whose expression was modified uniquely by the “tumor-inducing” Meis1 or tumor-inhibiting Prep1 overexpression. In both signatures, the enriched gene categories are the same and are involved in signal transduction. However, Meis1 targets stimulatory genes while Prep1 targets genes that inhibit the tumorigenic signaling pathways

A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome

Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a&nbsp;novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation