Biology subjects

Jain, S.

Publications and source records attributed to Jain, S..

11 recordsLinked to original sources

bioRxiv2018

Spatio-Temporal Network Dynamics of Genes Underlying Schizophrenia

Schizophrenia (SZ) is a debilitating mental illness with multigenic etiology and high heritability. Despite extensive genetic studies the molecular etiology stays enigmatic. A systems biology study had suggested a protein-protein interaction (PPI) network for SZ with 504 novel PPIs amongst which several genes happen to be drug targets of existing FDA approved drugs. Although the PPI network presented all possible pairs of interactions (known and novel), it lacks a spatio-temporal information. The onset of psychiatric disorders is predominantly in adolescent and young adult stages, often accompanied by subtle structural abnormalities in multiple regions of the brain. Hence, there is a need to redefine the generic PPI network as a function of time (developmental stages) and space (brain regions). The availability of BrainSpan atlas data allowed us to redefine the SZ interactome as a function of space and time. The absence of non-synonymous variants in centenarians and non-psychiatric ExAC database allowed us to identify the variants of criticality. The expression of candidate genes in different brain regions and during developmental stages, responsible for cognitive processes as well as the onset of disease were studied. A subset of novel interactors detected in the network was further validated using gene-expression data of psychiatric postmortem brains. From the long list of drug targets proposed from the interactome study and based on the microarray gene-expression results, we have shortlisted a probable subset of 10 drug targets (targeted by 34 FDA approved drugs) coalescing into 81 biological pathways, that could be potentially repurposed for neuropsychiatric disorders.

bioinformatics↗

bioRxiv2018

Prospective Study of Polygenic Risk, Protective Factors, and Incident Depression Following Combat Deployment in US Army Soldiers

BackgroundWhereas genetic susceptibility increases risk for major depressive disorder (MDD), non-genetic protective factors may mitigate this risk. In a large-scale prospective study of US Army soldiers, we examined whether trait resilience and/or unit cohesion could buffer against the onset of MDD following combat deployment, even in soldiers at high polygenic risk.\n\nMethodsData were analyzed from 4,182 soldiers of European ancestry assessed before and after their deployment to Afghanistan. Incident MDD was defined as no MDD episode at predeployment, followed by a MDD episode following deployment. Polygenic risk scores were constructed from the largest available MDD genome-wide association study. We first examined main effects of the MDD PRS and each protective factor on incident MDD. We then tested effects of each protective factor on incident MDD across strata of polygenic risk.\n\nResultsPolygenic risk showed a dose-response relationship to depression, such that soldiers at high polygenic risk had greatest odds for incident MDD. Both unit cohesion and trait resilience were prospectively associated with reduced risk for incident MDD. Notably, the protective effect of unit cohesion persisted even in soldiers at highest polygenic risk.\n\nConclusionsPolygenic risk was associated with new-onset MDD in deployed soldiers. However, unit cohesion--an index of perceived support and morale--was protective against incident MDD even among those at highest genetic risk, and may represent a potent target for promoting resilience in vulnerable soldiers. Findings illustrate the value of combining genomic and environmental data in a prospective design to identify robust protective factors for mental health.

genetics↗

bioRxiv2018

Integrated soybean transcriptomics, metabolomics, and chemical genomics reveal the importance of the phenylpropanoid pathway and antifungal activity in resistance to the broad host range pathogen Sclerotinia sclerotiorum

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield limiting disease of soybean called Sclerotinia stem rot (SSR). Resistance mechanisms against SSR are poorly understood, thus hindering the commercial deployment of SSR resistant varieties. We used a multiomic approach utilizing RNA-sequencing, Gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S. sclerotiorum in soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant, and one susceptible to S. sclerotiorum were analyzed in a time course experiment. The combined results show that resistance to S. sclerotiorum in soybean is associated in part with an early accumulation of JA-Ile ((+)-7-iso-Jasmonoyl-L-isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species (ROS), and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates such as, 4-hydroxybenzoate, ferulic acid and caffeic acid were highly accumulated in the resistant line. In vitro assays show that these metabolites and total stem extracts from the resistant line clearly affect S. sclerotiorum growth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance to S. sclerotiorum in soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.\n\nAuthor SummaryResistance to plant fungal pathogens with predominately necrotrophic lifestyles is poorly understood. In this study, we use Sclerotinia sclerotiorum and soybean as a model system to identify key resistance components in this crop plant. We employed a variety of omics approaches in combination with functional studies to identify plant processes associated with resistance to S. sclerotiorum. Our results suggest that resistance to this pathogen is associated in part with an earlier induction of jasmonate signaling, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway resulting in increased antifungal activities. These findings provide specific plant targets that can exploited to confer resistance to S. sclerotiorum and potentially other pathogens with similar lifestyle.

plant biology↗

bioRxiv2018

The Evolution of White Matter Microstructural Changes After Mild Traumatic Brain Injury: A Longitudinal DTI and NODDI Study

Neuroimaging biomarkers show promise for improving precision diagnosis and prognosis after mild traumatic brain injury (mTBI), but none has yet been adopted in routine clinical practice. Biophysical modeling of multishell diffusion MRI, using the neurite orientation dispersion and density imaging (NODDI) framework, may improve upon conventional diffusion tensor imaging (DTI) in revealing subtle patterns of underlying white matter microstructural pathology, such as diffuse axonal injury (DAI) and neuroinflammation, that are important for detecting mTBI and determining patient outcome. With a cross-sectional and longitudinal design, we assessed structural MRI, DTI and NODDI in 40 mTBI patients at 2 weeks and 6 months after injury and 14 matched control participants with orthopedic trauma but not suffering from mTBI at 2 weeks. Self-reported and performance-based cognitive measures assessing postconcussive symptoms, memory, executive functions and processing speed were investigated in post-acute and chronic phase after injury for the mTBI subjects. Machine learning analysis was used to identify mTBI patients with the best neuropsychological improvement over time and relate this outcome to DTI and NODDI biomarkers. In the cross-sectional comparison with the trauma control group at 2 weeks post-injury, mTBI patients showed decreased fractional anisotropy (FA) and increased mean diffusivity (MD) on DTI mainly in anterior tracts that corresponded to white matter regions of elevated free water fraction (FISO) on NODDI, signifying vasogenic edema. Patients showed decreases from 2 weeks to 6 months in white matter neurite density on NODDI, predominantly in posterior tracts. No significant longitudinal changes in DTI metrics were observed. The machine learning analysis divided the mTBI patients into two groups based on their recovery. Voxel-wise group comparison revealed associations between white matter orientation dispersion index (ODI) and FISO with degree and trajectory of improvement within the mTBI group. In conclusion, white matter FA and MD alterations early after mTBI might reflect vasogenic edema, as shown by elevated free water on NODDI. Longer-term declines in neurite density on NODDI suggest progressive axonal degeneration due to DAI, especially in tracts known to be integral to the structural connectome. Overall, these results show that the NODDI parameters appear to be more sensitive to longitudinal changes than DTI metrics. Thus, NODDI merits further study in larger cohorts for mTBI diagnosis, prognosis and treatment monitoring.

neuroscience↗

bioRxiv2018

Incorporating Context into Language Encoding Models for fMRI

Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding. In this work, we instead build encoding models using rich contextual representations derived from an LSTM language model. Our models show a significant improvement in encoding performance relative to state-of-the-art embeddings in nearly every brain area. By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information. We are also able to use our models to map context sensitivity across the cortex. These results suggest that LSTM language models learn high-level representations that are related to representations in the human brain.

neuroscience↗

bioRxiv2018

INDEX-db: The Indian Exome Reference database (Phase-I)

Deep sequencing based genetic mapping has greatly enhanced the ability to catalog variants with plausible disease association. The bigger challenge now is to ascertain pathological significance to the array of identified variants to specific disease conditions. Differential selection pressure may impact frequency of genetic variations, and thus the detection of association with disease conditions, across populations. To understand the genotype to phenotype correlations, it thus becomes important to first understand the genetic variation spectrum of a population by creating a reference map. In this study, we report the development of phase I of a new database of coding variations, from the Indian population, with an aim to establish a centralized database of integrated information. This could be useful for researchers involved in studying disease mechanism at the clinical, genetic and cellular level.\n\nDatabase URL: http://indexdb.ncbs.res.in

genomics↗

bioRxiv2018

Exome sequencing in families with severe mental illness identifies novel and rare variants in genes implicated in Mendelian neuropsychiatric syndromes

IntroductionSevere Mental Illnesses (SMI), such as bipolar disorder and schizophrenia, are highly heritable, and have a complex pattern of inheritance. Genome wide association studies detect a part of the heritability, which can be attributed to common genetic variation. Examination of rare variants with Next Generation Sequencing (NGS) may add to the understanding of genetic architecture of SMIs.\n\nMethodsWe analyzed 32 ill subjects (with diagnosis of Bipolar Disorder, n=26; schizophrenia, n=4; schizoaffective disorder, n=1 schizophrenia like psychosis, n=1) from 8 multiplex families; and 33 healthy individuals by whole exome sequencing. Prioritized variants were selected by a 4-step filtering process, which included deleteriousness by 5 in silico algorithms; sharing within families, absence in the controls and rarity in South Asian sample of Exome Aggregation Consortium.\n\nResultsWe identified a total of 42 unique rare, non-synonymous deleterious variants in this study with an average of 5 variants per family. None of the variants were shared across families, indicating a private mutational profile. Twenty (47.6%) of the variant harboring genes identified in this sample have been previously reported to contribute to the risk of neuropsychiatric syndromes. These include genes which are related to neurodevelopmental processes, or have been implicated in different monogenic syndromes with a severe neurodevelopmental phenotype.\n\nConclusionNGS approaches in family based studies are useful to identify novel and rare variants in genes for complex disorders like SMI. The study further validates the phenotypic burden of rare variants in Mendelian disease genes, indicating pleiotropic effects in the etiology of severe mental illnesses.

neuroscience↗

bioRxiv2017

Macrophage-to-sensory neuron crosstalk mediated by Angiotensin II type-2 receptor elicits neuropathic pain

Peripheral nerve damage initiates a complex series of cellular and structural processes that culminate in chronic neuropathic pain. Our study defines local angiotensin signaling via activation of the Angiotensin II (Ang II) type-2 receptor (AT2R) on macrophages as the critical trigger of neuropathic pain. An AT2R-selective antagonist attenuates neuropathic, but not inflammatory pain hypersensitivity in mice, and requires the cell damage-sensing ion channel transient receptor potential family-A member-1 (TRPA1). Mechanical and cold pain hypersensitivity that are characteristic of neuropathic conditions can be attenuated by chemogenetic depletion of peripheral macrophages and AT2R-null hematopoietic cell transplantation. Our findings show no AT2R expression in mouse or human sensory neurons, rather AT2R expression and activation in macrophages triggers production of reactive oxygen/nitrogen species, which trans-activate TRPA1 on sensory neurons. Our study defines the precise neuro-immune crosstalk underlying nociceptor sensitization at the site of nerve injury. This form of cell-to-cell signaling represents a critical peripheral mechanism for chronic neuropathic pain, and therefore identifies multiple analgesic targets.

neuroscience↗

bioRxiv2017

Stability of Commonly Used Haematological Parameters in Samples Stored at 33°C, 22°C and 4°C

AimThis study aimed to investigate the analytical bias and imprecision in haematological parameters induced by storage at 4{degrees}C, 22{degrees}C and 33 {degrees}C.\n\nMethodsThree K2EDTA anticoagulated vials of blood were collected from each of twenty blood donors and stored at 4{degrees}C, 22{degrees}C and 33{degrees}C respectively. Readings from each vial were taken at 0, 4, 6, 12, 24, 48 and 72 hours after collection on the Sysmex XP-100 analyser. The mean and median shift of the parameters relative to the baseline and the coefficient of variation for each time-temperature combination were calculated. The shift was compared to the maximum acceptable bias.\n\nResultsHaemoglobin, Red Blood Cell Count, White Blood Cell Count, Mean Corpuscular Haemoglobin were stable for at least twenty four hours at 33{degrees}C. Haematocrit, Mean Corpuscular Volume and Platelet Counts were stable for less than four hours at 33{degrees}C. All the above parameters were stable for longer at 22{degrees}C and 4{degrees}C. The three-part differential count showed instability within four hours at 33 {degrees}C.\n\nConclusionsStrict pre-analytical control is needed at 33{degrees}C or above due to the marked instability of most parameters. However, Haemoglobin, Red Blood Cell Count, White Blood Cell Count and Mean Corpuscular Haemoglobin remain relatively stable even at 33{degrees}C.\n\nKey MessageHaematology samples exposed to temperatures of 33{degrees}C or above show rapid change in MCV, HCT,MCHC, RDW, Platelet Counts and three-part differential counts. Settings where prolonged exposure to these temperatures cannot be avoided should rely on the more stable parameters of Haemoglobin, RBC Counts, MCH and WBC Counts.

pathology↗

bioRxiv2017

Using Neural Networks To Improve Single-Cell RNA-Seq Data Analysis

While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges including questions related to handling the noisy and sometimes incomplete data, how to identify unique group of cells in such experiments and how to determine the state or function of specific cells based on their expression profile. To address these issues we develop and test a method based on neural networks (NN) for the analysis and retrieval of single cell RNA-Seq data. We tested various NN architectures, some biologically motivated, and used these to obtain a reduced dimension representation of the single cell expression data. We show that the NN method improves upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of tens of thousands of single cell profiles. Such database queries (which can be performed using our web server) will enable researchers to better characterize cells when analyzing heterogeneous scRNA-Seq samples.\n\nSupporting website: http://sb.cs.cmu.edu/scnn/\n\nPassword for accessing the retrieval task webserver: scRNA-Seq

bioinformatics↗

bioRxiv2016

A Pilot Study: Development of a Reference Spatiotemporal Gait Data Set for Indian Subjects.

1.Interpretation of pathological gait has, for decades, offered insight into amputee anomalies and diseases such as cerebral palsy. Gait analysis has been actively used at the Bhagwan Mahaveer Viklang Sahatya Samiti for such purposes. The normative gait data used to compare data obtained from amputees, however, has been collected from laboratories under dissimilar conditions, skewing interpretation. Spatiotemporal gait parameters were extractedfrom 43 male Indian subjects using a 7.5m walkway and the BTS Bioengineering GAITLAB setup. Working under the hypothesis that a lack of cross-cultural validity was somewhat responsible for variations in normative gait, we attempted to develop a region-specific data set for use at the headquarters of the Jaipur Foot Organization Bhagwan Mahaveer Viklang Sahatya Samiti (BMVSS). Stratified random sampling was used to recruit subjects and measures were taken to ensure minimal effects of extraneous variables. Statistical analysis was performed on obtained data using one-way analysis of variance (ANOVA) to gauge the effect age and ethnicity had on normative values of the parameters investigated. We found statistically significant p-values for a few spatiotemporal parameters in the analysis of variance for both age and ethnicity. While the results were much less significant than initially hypothesized, the study proved an efficient way to create a normative gait data set exclusively for use at the gait laboratory of the Jaipur Foot Organization, thereby eliminating potential erroneous interpretation of pathological gait when comparing said gait to normative gait data obtained in laboratories under dissimilar conditions.

bioengineering↗