PCAT: an integrated portal for genomic and preclinical testing data of pediatric cancer patient-derived xenograft models.

Although cancer is the leading cause of disease-related mortality in children, the relative rarity of pediatric cancers poses a significant challenge for developing novel therapeutics to further improve prognosis. Patient-derived xenograft (PDX) models, which are usually developed from high-risk tumors, are a useful platform to study molecular driver events, identify biomarkers and prioritize therapeutic agents. Here, we develop PDX for Childhood Cancer Therapeutics (PCAT), a new integrated portal for pediatric cancer PDX models. Distinct from previously reported PDX portals, PCAT is focused on pediatric cancer models and provides intuitive interfaces for querying and data mining. The current release comprises 324 models and their associated clinical and genomic data, including gene expression, mutation and copy number alteration. Importantly, PCAT curates preclinical testing results for 68 models and 79 therapeutic agents manually collected from individual agent testing studies published since 2008. To facilitate comparisons of patterns between patient tumors and PDX models, PCAT curates clinical and molecular data of patient tumors from the TARGET project. In addition, PCAT provides access to gene fusions identified in nearly 1000 TARGET samples. PCAT was built using R-shiny and MySQL. The portal can be accessed at http://pcat.zhenglab.info or http://www.pedtranscriptome.org.

Isolation and characterization of culturable endophytic bacterial community of stripe rust-resistant and stripe rust-susceptible Pakistani wheat cultivars.

In this study, endophytic bacteria isolated from root, stem, and leaf tissues of stripe rust-susceptible (Inqilab 91, Galaxy 2013, and 15BT023) and stripe rust-resistant (NARC 2011, Ujala 2015, TW1410) cultivars were identified and characterized. Abundance of endophytes was found in roots as compared with stems and leaves. Resistant and susceptible cultivars significantly differed in abundance of endophytic bacteria. Restriction analysis of 16S rRNA genes amplified from 100 bacterial isolates produced 17 unique patterns. Representatives of each of the 17 unique patterns were sequenced and identified. Among the sequenced bacteria, 8 belonged to Firmicutes, 7 were Proteobacteria, and 2 were Actinobacteria. Most of the isolates have plant growth-promoting properties and a few have the potential of producing hydrolytic enzymes. Two isolates showed significant inhibition of rust spore germination. These endophytic bacteria not only can be helpful in growth-promoting activities but also can assist in biocontrol of stripe rust disease.

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways.

BACKGROUND: Neurodegeneration is a progressive/irreversible loss of neurons, building blocks of our nervous system. Their degeneration gradually collapses the entire structural and functional system manifesting in myriads of clinical disorders categorized as Neurodegenerative Disorders (NDs) such as Alzheimer's Disease, (AD), Parkinson's Disease (PD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). NDs are characterized by a puzzling interplay of molecular and cellular defects affecting subset of neuronal populations in specific affected brain areas. OBJECTIVE: In present study, comparative in silico analysis was performed by utilizing gene expression datasets of AD, PD, FTD and ALS to identify potential common features to gain insights into complex molecular pathophysiology of the selected NDs. METHODS: Gene expression data of four disorders were subjected to the identification of Differential Gene Expression (DEG) and their mapping on biological processes, KEGG pathways and molecular functions. Detailed comparative analysis was performed to highlight the common grounds of these dis-orders at various stages. RESULTS: Astoundingly, 106 DEGs were found to be common across all disorders. Alongwith in total 100 GO terms and 7 KEGG pathways were found to be significantly enriched across all disorders. EGFR, CDC42 and CREBBP have been identified as the significantly interacting nodes in gene-gene in-teraction and in Protein-Protein Interaction (PPI) network as well. Furthermore, interaction of common DEGs targets with miRNA's has been scrutinized. CONCLUSION: The complex molecular underpinnings of these disorders are currently elusive. Despite heterogeneous clinical and pathological expressions, common features have been recognized in many NDs which provide evidence of their convergence.

ChromBiSim: Interactive chromatin biclustering using a simple approach.

Combinatorial patterns of histone modifications sketch the epigenomic locale. Specific positions of these modifications in the genome are marked by the presence of such signals. Various methods highlight such patterns on global scale hence missing the local patterns which are the actual hidden combinatorics. We present ChromBiSim, an interactive tool for mining subsets of modifications from epigenomic profiles. ChromBiSim efficiently extracts biclusters with their genomic locations. It is the very first user interface based and multiple cell type handling tool for decoding the interplay of subsets of histone modifications combinations along their genomic locations. It displays the results in the forms of charts and heat maps in accordance with saving them in files which could be used for post analysis. ChromBiSim tested on multiple cell types produced in total 803 combinatorial patterns. It could be used to highlight variations among diseased versus normal cell types of any species. AVAILABILITY: ChromBiSim is available at (http://sourceforge.net/projects/chrombisim) in C-sharp and python languages.

ChromClust: A semi-supervised chromatin clustering toolkit for mining histone modifications interplay.

Mining patterns of histone modifications interplay from epigenomic profiles are one of the leading research areas these days. Various methods based on clustering approaches and hidden Markov models have been presented so far with some limitations. Here we present ChromClust, a semi-supervised clustering tool for mining commonly occurring histone modifications at various locations of the genome. Applying our method to 11 chromatin marks in nine human cell types recovered 11 clusters based on distinct chromatin signatures mapping to various elements of the genome. Our approach is efficient in respect to time and space usage along with the added facility of maintaining database at the backend. It outperforms the existing methods with respect to mining patterns in a semi-supervised fashion mapping to various functional elements of the genome. It will aid in future by saving the resources of time and space along with efficiently retrieving the hidden interplay of histone combinations.

Computational design of anti-cancer peptides tailored to target specific tumor markers.

Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics.

OpenPBTA: The Open Pediatric Brain Tumor Atlas.

Pediatric brain and spinal cancers are collectively the leading disease-related cause of death in children; thus, we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Children's Brain Tumor Network (CBTN) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to establish OpenPBTA, an open collaborative project with over 40 scalable analysis modules that genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas and TP53 loss as a significant marker for poor overall survival in ependymomas and H3 K28-mutant diffuse midline gliomas. Already being actively applied to other pediatric cancers and PNOC molecular tumor board decision-making, OpenPBTA is an invaluable resource to the pediatric oncology community.

Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer.

Scoring gene signatures is common for bulk and single-cell RNA sequencing (scRNAseq) data. Here, using cancer as a data model, we describe steps to benchmark signature scoring techniques for scRNAseq data in the context of uneven gene dropouts. These steps include identifying and comparing deregulated signatures, generating gold standard signatures for specificity and sensitivity tests, and simulating the impact of dropouts using down sampling. The protocol provides a framework for benchmarking scRNAseq algorithms in such context. For complete details on the use and execution of this protocol, please refer to Noureen et al. (2022).1.

Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data.

Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, few attempts have been made to benchmark these methods. Here, we benchmark five such methods, including single sample gene set enrichment analysis (ssGSEA), Gene Set Variation Analysis (GSVA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

Integrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation.

Active telomerase is essential for stem cells and most cancers to maintain telomeres. The enzymatic activity of telomerase is related but not equivalent to the expression of TERT, the catalytic subunit of the complex. Here we show that telomerase enzymatic activity can be robustly estimated from the expression of a 13-gene signature. We demonstrate the validity of the expression-based approach, named EXTEND, using cell lines, cancer samples, and non-neoplastic samples. When applied to over 9,000 tumors and single cells, we find a strong correlation between telomerase activity and cancer stemness. This correlation is largely driven by a small population of proliferating cancer cells that exhibits both high telomerase activity and cancer stemness. This study establishes a computational framework for quantifying telomerase enzymatic activity and provides new insights into the relationships among telomerase, cancer proliferation, and stemness.

Computational Protein-Protein Docking Reveals the Therapeutic Potential of Kunitz-type Venom against hKv1.2 Binding Sites.

BACKGROUND & OBJECTIVE: Kunitz-type venoms are bioactive proteins isolated from a wide variety of venomous animals. These venoms are involved in protease inhibitory activity or potassium channel blocking activity. Therefore, they are reported as an important source for lead drug candidates towards protease or channel associated diseases like neurological, metabolic and cardiovascular disorders. METHODS: This study aimed to check the inhibitory action of Kunitz-type venoms against potassium channels using computational tools. RESULTS: Among potassium channels, Human Voltage-Gated Potassium Channel 1.2 (hKv1.2) was used as a receptor whereas Kunitz-type peptides from the venoms of various species were selected as ligand dataset. CONCLUSION: This study helped in finding the binding interface between the receptor and ligand dataset for their potential therapeutic use in treating potassium channelopathies.

Structure Activity Relationship of Venom Toxins Targeting Potassium Channels.

BACKGROUND: Peptide toxins are naturally occurring rich sources of highly specific bioactive compounds from venomous animals acting on various types of ion channels. OBJECTIVE: This study mainly highlights targeting of one of the largest families of ion channels i.e. potassium channels via venom toxins. METHOD: Data for reported venom toxins from diverse species is gathered and analyzed at sequence and structural extent. RESULTS: The similarities and differences among toxins have been demonstrated along with structure activity relationship of potassium channels with these toxins. CONCLUSION: This review highlights the importance of functionally important residues and structural scaffolds of venoms interacting with potassium channels.

MCaVoH: A toolkit for mining, classification and visualization of human microbiota.

Human body is the home for a large number of microbes. The complexity of enterotype depends on the body site. Microbial communities in various samples from different regions are being classified on the basis of 16S rRNA gene sequences. With the improvement in sequencing technologies various computational methods have been used for the analysis of microbiome data. Despite several available machine learning techniques there is no single platform available which could provide several techniques for clustering, multiclass classification, comparative analysis and the most significantly the identification of the subgroups present within larger groups of human microbial communities. We present a tool named MCaVoH for this purpose which performs clustering and classification of 16S rRNA sequence data and highlight various groups. Our tool has an added facility of biclustering which produces local group of communities present within larger groups (clusters). The core objective of our work was to identify the interaction between various bacterial species along with monitoring the composition and variations in microbial communities. MCaVoH also evaluates the performance and efficiency of different techniques using comparative analysis. The results are visualized through different plots and graphs. We implemented our tool in MATLAB. We tested our tool on several real and simulated 16S rRNA data sets and it outperforms several existing methods. Our tool provides a single platform for using multiple clustering, classification algorithms, local community identification along with their comparison which has not been done so far. Tool is available at https://sourceforge.net/projects/mcavoh/.

Antibiotic susceptibility profiling and virulence potential of Campylobacter jejuni isolates from different sources in Pakistan.

OBJECTIVE: To determine antibiotic resistance patterns and virulence potential of Campylobacter jejuni (C. jejuni) isolates from clinical human diarrheal infections, cattle and healthy broilers. METHODS: Antibiotic sensitivity patterns of C. jejuni isolates were determined by Kirby Bauer Disc Diffusion assay. These isolates were then subjected to virulence profiling for the detection of mapA (membrane-associated protein), cadF (fibronectin binding protein), wlaN (beta-l,3-galactosyltransferase) and neuAB (sialic acid biosynthesis gene). Further C. jejuni isolates were grouped by random amplification of polymorphic DNA (RAPD) profiling. RESULTS: A total of 436 samples from poultry (n=88), cattle (n=216) and humans (n=132) from different locations were collected. Results revealed percentage of C. jejuni isolates were 35.2% (31/88), 25.0% (54/216) and 11.3% (15/132) among poultry, cattle and clinical human samples respectively. Antibiotic susceptibility results showed that similar resistance patterns to cephalothin was ie. 87.0%, 87.1% and 89%among humans, poultry and cattle respectively, followed by sulfamethoxazole+trimethoprim 40.0%, 38.7% and 31.0% in humans, poultry and cattle and Ampicillin 40%, 32% and 20% in humans, poultry and cattle respectively. Beta-lactamase activity was detected in 40.00% humans, 20.37% cattle and 32.25% in poultry C. jejuni isolates. CadF and mapA were present in all poultry, cattle and human C. jejuni isolates, wlaN was not detected in any isolate and neuAB was found in 9/31 (36%) poultry isolates. RAPD profiling results suggested high diversity of C. jejuni isolates. CONCLUSIONS: Detection of multidrug resistant C. jejuni strains from poultry and cattle is alarming as they can be potential hazard to humans. Moreover, predominant association of virulence factors, cadF and mapA (100% each) in C. jejuni isolates from all sources and neuAB (36%) with poultry isolates suggest the potential source of transmission of diverse types of C. jejuni to humans.

Draft Genome Sequence of the Enteropathogenic Bacterium Campylobacter jejuni Strain cj255.

The enteropathogen Campylobacter jejuni is a global health disaster, being one of the leading causes of bacterial gastroenteritis. Here, we present the draft genome sequence of C. jejuni strain cj255, isolated from a chicken source in Islamabad, Pakistan. The draft genome sequence will aid in epidemiological studies and quarantine of this broad-host-range pathogen.

The genomic landscape of the somatic linker histone subtypes H1.1 to H1.5 in human cells.

Human cells contain five canonical, replication-dependent somatic histone H1 subtypes (H1.1, H1.2, H1.3, H1.4, and H1.5). Although they are key chromatin components, the genomic distribution of the H1 subtypes is still unknown, and their role in chromatin processes has thus far remained elusive. Here, we map the genomic localization of all somatic replication-dependent H1 subtypes in human lung fibroblasts using an integrative DNA adenine methyltransferase identification (DamID) analysis. We find in general that H1.2 to H1.5 are depleted from CpG-dense regions and active regulatory regions. H1.1 shows a DamID binding profile distinct from the other subtypes, suggesting a unique function. H1 subtypes can mark specific domains and repressive regions, pointing toward a role for H1 in three-dimensional genome organization. Our work integrates H1 subtypes into the epigenome maps of human cells and provides a valuable resource to refine our understanding of the significance of H1 and its heterogeneity in the control of genome function.

Identification of type-specific anticancer histone deacetylase inhibitors: road to success.

Cancer is a serious and life-eliminating disease. Majority of anticancer agents are non-selective. Along with the cancerous cells they also target the normal ones. An important aspect is to hit the developing mechanism of the tumor, which is highlighted by in silico drug designing. On the basis of novel molecular targets, in silico (computational approach) drug discovery has emerged as today's need. Histone deacetylases are an important therapeutic target for many human cancers. The first and only approved (in 2006) histone deacetylase inhibitors (HDACIs) is Zolinza. Depending on the types of the histone deacetylase (HDAC) enzymes, discovery of type-specific inhibitors is important. With continued research and development, in near future HDACIs are likely to figure prominently in cancer treatment plans. This review presents the overview of HDACs, their role in cancer, their structural classes, activity, catalytic domain and the inhibitors of HDACs for cancer therapy. Also it helps in understanding the open directions in this area of research and highlights the importance of computational approaches in discovering specific drugs for cancer therapy.

Isolation and characterization of culturable endophytic bacterial community of stripe rust-resistantand striperust-susceptible Pakistani wheat cultivars

In this study, endophytic bacteria isolated from root, stem, and leaf tissues of stripe rust-susceptible (Inqilab 91, Galaxy 2013, and 15BT023) and stripe rust-resistant (NARC 2011, Ujala 2015, TW1410) cultivars were identified and characterized. Abundance of endophytes was found in roots as compared with stems and leaves. Resistant and susceptible cultivars significantly differed in abundance of endophytic bacteria. Restriction analysis of 16S rRNA genes amplified from 100 bacterial isolates produced 17 unique patterns. Representatives of each of the 17 unique patterns were sequenced and identified. Among the sequenced bacteria, 8 belonged to Firmicutes, 7 were Proteobacteria, and 2 were Actinobacteria. Most of the isolates have plant growth-promoting properties and a few have the potential of producing hydrolytic enzymes. Two isolates showed significant inhibition of rust spore germination. These endophytic bacteria not only can be helpful in growth-promoting activities but also can assist in biocontrol of stripe rust disease

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