Raghvendra Mall

A metabolic function of FGFR3-TACC3 gene fusions in cancer

Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3–T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3–T3 in many other cancers, indicating that F3–T3 is a commonly occuring fusion across all tumour types. F3–T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors, but the downstream oncogenic signalling pathways remain unknown. Here we show that human tumours with F3–T3 fusions cluster within transcriptional subgroups that are characterized by the activation of mitochondrial functions. F3–T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. Phosphorylation of the phosphopeptide PIN4 is an intermediate step in the signalling pathway of the activation of mitochondrial metabolism. The F3–T3–PIN4 axis triggers the biogenesis of peroxisomes and the synthesis of new proteins. The anabolic response converges on the PGC1α coactivator through the production of intracellular reactive oxygen species, which enables mitochondrial respiration and tumour growth. These data illustrate the oncogenic circuit engaged by F3–T3 and show that F3–T3-positive tumours rely on mitochondrial respiration, highlighting this pathway as a therapeutic opportunity for the treatment of tumours with F3–T3 fusions. We also provide insights into the genetic alterations that initiate the chain of metabolic responses that drive mitochondrial metabolism in cancer.

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

Abstract We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.

Detection of statistically significant network changes in complex biological networks

BackgroundBiological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks.MethodsIn this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation.ResultsIn the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.ConclusionsWe show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.

PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine

MotivationnProtein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought.nnnResultsnIn this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthews correlation coefficient, with an overall accuracy of 74% and Matthews correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability.nnnAvailability and implementationnPaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP).nnnContactngwo-yu.chuang@nih.gov.nnnSupplementary informationnSupplementary data are available at Bioinformatics online.

DeepSol: a deep learning framework for sequence-based protein solubility prediction

Motivation: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence‐based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning‐based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k‐mer structure and additional sequence and structural features extracted from the protein sequence. Results: DeepSol outperformed all known sequence‐based state‐of‐the‐art solubility prediction methods and attained an accuracy of 0.77 and Matthews correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins. Availability and implementation: DeepSols best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018). Supplementary information: Supplementary data are available at Bioinformatics online.

Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

BackgroundHuman tissues are invaluable resources for researchers worldwide. Biobanks are repositories of such human tissues and can have a strategic importance for genetic research, clinical care, and future discoveries and treatments. One of the aims of Qatar Biobank is to improve the understanding and treatment of common diseases afflicting Qatari population such as obesity and diabetes.MethodsIn this study we apply a panorama of state-of-the-art statistical methods and machine learning algorithms to investigate associations and risk factors for diabetes and obesity on a sample of 1000 Qatari population.ResultsRegarding diabetes, we identified pronounced associations and risk factors in Qatari population including magnesium, chloride, c-peptide of insulin, insulin, and uric acid. Similarly, for obesity, significant associations and risk factors include insulin, c-peptide of insulin, albumin, and uric acid. Moreover, our study has revealed interactions of hypomagnesemia with HDL-C, triglycerides, and free thyroxine.ConclusionsOur study strongly confirms known associations and risk factors associated with diabetes and obesity in Qatari population as previously found in other population studies in different parts of the world. Moreover, interactions of hypomagnesemia with other associations and risk factors merit further investigations.

Accurate Prediction of Antibody Resistance in Clinical HIV-1 Isolates

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection with several undergoing clinical trials. Due to high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to particular bNAbs. Resistant strains are commonly identified by time-consuming and expensive in vitro neutralization experiments. Here, we developed machine learning-based classifiers that accurately predict resistance of HIV-1 strains to 33 neutralizing antibodies. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of the tree-based machine learning method gradient boosting machine enabled us to identify critical epitope features that distinguish between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor will facilitate informed decisions of antibody usage in clinical settings.

Correction to: Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

Following publication of the original article [1], the authors reported that one of the authors’ names was processed incorrectly. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.

Fourier-Transform Infrared Imaging Spectroscopy and Laser Ablation -ICPMS New Vistas for Biochemical Analyses of Ischemic Stroke in Rat Brain

Objective: Stroke is the main cause of adult disability in the world, leaving more than half of the patients dependent on daily assistance. Understanding the post-stroke biochemical and molecular changes are critical for patient survival and stroke management. The aim of this work was to investigate the photo-thrombotic ischemic stroke in male rats with particular focus on biochemical and elemental changes in the primary stroke lesion in the somatosensory cortex and surrounding areas, including the corpus callosum. Materials and Methods: FT-IR imaging spectroscopy and LA-ICPMS techniques examined stroke brain samples, which were compared with standard immunohistochemistry studies. Results: The FTIR results revealed that in the lesioned gray matter the relative distribution of lipid, lipid acyl and protein contents decreased significantly. Also at this locus, there was a significant increase in aggregated protein as detected by high-levels Aβ1-42. Areas close to the stroke focus experienced decrease in the lipid and lipid acyl contents associated with an increase in lipid ester, olefin, and methyl bio-contents with a novel finding of Aβ1-42 in the PL-GM and L-WM. Elemental analyses realized major changes in the different brain structures that may underscore functionality. Conclusion: In conclusion, FTIR bio-spectroscopy is a non-destructive, rapid, and a refined technique to characterize oxidative stress markers associated with lipid degradation and protein denaturation not characterized by routine approaches. This technique may expedite research into stroke and offer new approaches for neurodegenerative disorders. The results suggest that a good therapeutic strategy should include a mechanism that provides protective effect from brain swelling (edema) and neurotoxicity by scavenging the lipid peroxidation end products.

Differential Community Detection in Paired Biological Networks

Motivation: Biological networks unravel the inherent structure of molecular interactions which can lead to discovery of driver genes and meaningful pathways especially in cancer context. Often due to gene mutations, the gene expression undergoes changes and the corresponding gene regulatory network sustains some amount of localized re-wiring. The ability to identify significant changes in the interaction patterns caused by the progression of the disease can lead to the revelation of novel relevant signatures. Methods: The task of identifying differential sub-networks in paired biological networks (A:control,B:case) can be re-phrased as one of finding dense communities in a single noisy differential topological (DT) graph constructed by taking absolute difference between the topological graphs of A and B. In this paper, we propose a fast three-stage approach, namely Differential Community Detection (DCD), to identify differential sub-networks as differential communities in a de-noised version of the DT graph. In the first stage, we iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix using neighbourhood information of the nodes and Jaccard similarity. In the second stage, the ordered DT adjacency matrix is traversed along the diagonal to remove all the edges associated with a node, if that node has no immediate edges within a window. Finally, we apply community detection methods on this de-noised DT graph to discover differential sub-networks as communities. Results: Our proposed DCD approach can effectively locate differential sub-networks in several simulated paired random-geometric networks and various paired scale-free graphs with different power-law exponents. The DCD approach easily outperforms community detection methods applied on the original noisy DT graph and recent statistical techniques in simulation studies. We applied DCD method on two real datasets: a) Ovarian cancer dataset to discover differential DNA co-methylation sub-networks in patients and controls; b) Glioma cancer dataset to discover the difference between the regulatory networks of IDH-mutant and IDH-wild-type. We demonstrate the potential benefits of DCD for finding network-inferred bio-markers/pathways associated with a trait of interest. Conclusion: The proposed DCD approach overcomes the limitations of previous statistical techniques and the issues associated with identifying differential sub-networks by use of community detection methods on the noisy DT graph. This is reflected in the superior performance of the DCD method with respect to various metrics like Precision, Accuracy, Kappa and Specificity. The code implementing proposed DCD method is available at https://sites.google.com/site/raghvendramallmlresearcher/codes.