Biology subjects

Singh, R.

Publications and source records attributed to Singh, R..

At least 271 records · Page 15Linked to original sources

bioRxiv2020

Gromov-Wasserstein optimal transport to align single-cell multi-omics data

Data integration of single-cell measurements is critical for understanding cell development and disease, but the lack of correspondence between different types of measurements makes such efforts challenging. Several unsupervised algorithms can align heterogeneous single-cell measurements in a shared space, enabling the creation of mappings between single cells in different data domains. However, these algorithms require hyperparameter tuning for high-quality alignments, which is difficult in an unsupervised setting without correspondence information for validation. We present Single-Cell alignment using Optimal Transport (SCOT), an unsupervised learning algorithm that uses Gromov Wasserstein-based optimal transport to align single-cell multi-omics datasets. We compare the alignment performance of SCOT with state-of-the-art algorithms on four simulated and two real-world datasets. SCOT performs on par with state-of-the-art methods but is faster and requires tuning fewer hyperparameters. Furthermore, we provide an algorithm for SCOT to use Gromov Wasserstein distance to guide the parameter selection. Thus, unlike previous methods, SCOT aligns well without using any orthogonal correspondence information to pick the hyperparameters. Our source code and scripts for replicating the results are available at https://github.com/rsinghlab/SCOT.

bioinformatics↗

bioRxiv2020

FastSK: Fast Sequence Analysis with Gapped String Kernels

Gapped k-mer kernels with Support Vector Machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly-sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on the sub-sequence feature-length, number of mismatch positions, and the tasks alphabet size. In this work, we introduce a fast and scalable algorithm for calculating gapped k-mer string kernels. Our method, named FastSK, uses a simplified kernel formulation that decomposes the kernel calculation into a set of independent counting operations over the possible mismatch positions. This simplified decomposition allows us to devise a fast Monte Carlo approximation that rapidly converges. FastSK can scale to much greater feature lengths, allows us to consider more mismatches, and is performant on a variety of sequence analysis tasks. On 10 DNA transcription factor binding site (TFBS) prediction datasets, FastSK consistently matches or outperforms the state-of-the-art gkmSVM-2.0 algorithms in AUC, while achieving average speedups in kernel computation of [~]100x and speedups of [~]800x for large feature lengths. We further show that FastSK outperforms character-level recurrent and convolutional neural networks across all 10 TFBS tasks. We then extend FastSK to 7 English-language medical named entity recognition datasets and 10 protein remote homology detection datasets. FastSK consistently matches or outperforms these baselines. Our algorithm is available as a Python package and as C++ source code1.

bioinformatics↗

bioRxiv2020

Structural Basis of CD4 Downregulation by HIV-1 Nef

The HIV-1 protein Nef suppresses multiple immune surveillance mechanisms to promote viral pathogenesis1. Individuals infected with HIV-1 encoding defective nef genes do not develop AIDS for decades2,3. A key target of Nef is the cellular receptor CD4. Although essential for viral entry into host cells, CD4 is problematic for the virus later in its replication cycle: CD4 disrupts processing of the viral glycoprotein, Env, inhibiting infectivity4; it interferes with the release of new virions5,6; and it causes vulnerability to superinfection, causing premature cell death and limiting viral productivity7. Furthermore, binding of CD4 to Env exposes otherwise-concealed Env epitopes, rendering infected cells more susceptible to antibody-dependent cellular cytotoxicity and virus particles more susceptible to neutralizing antibodies8-10. HIV-1 has evolved strategies to mitigate these problems. Newly synthesized CD4 is targeted in the endoplasmic reticulum by the viral Vpu protein for proteasomal degradation11. Surface-expressed CD4, in contrast, is targeted by Nef for endocytosis and lysosomal degradation12-15. Nefs effect on CD4 involves hijacking of clathrin adaptor complex 2 (AP2)-dependent endocytosis16,17. Although how Nef associates with a part of the tetrameric AP2 is understood18, a complete understanding of the interaction, especially how CD4 is sequestered by Nef into a complex with AP2, has remained elusive. Here, we present a high-resolution crystal structure that describes the underlying mechanism. An intricate combination of conformational changes occurs in both Nef and AP2 to enable CD4 binding and downregulation. Strikingly, a pocket on Nef previously identified as crucial for recruiting class I MHC is also responsible for recruiting CD4, revealing a potential approach to inhibit two of Nefs activities and sensitize the virus to immune clearance

biochemistry↗

bioRxiv2020

Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

genetics↗

bioRxiv2020

Omics-Based Interaction Framework - a systems model to reveal molecular drivers of synergy

Bioactive molecule library screening strategies may empirically identify effective combination therapies. However, without a systems theory to interrogate synergistic responses, the molecular mechanisms underlying favorable drug-drug interactions remain unclear, precluding rational design of combination therapies. Here, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in supra-additive biological responses. OBIF performs full factorial analysis of feature expression data from single vs. dual factor exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed a therapeutic dyad of immunostimulatory small molecules that induces synergistic protection against influenza A pneumonia. OBIF analysis of transcriptomic and proteomic data identified biologically relevant, unanticipated cooperation between RelA and cJun that we subsequently confirmed to be required for the synergistic antiviral protection. To demonstrate generalizability, OBIF was applied to data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, OBIF is a phenotype-driven systems model that supports multiplatform exploration of synergy mechanisms.

systems biology↗

bioRxiv2020

Identification of potential biomarkers in subtypes of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer. Due to the lack of specific symptoms, [~]80% of epithelial ovarian cancer is diagnosed at an advanced stage and often metastasize to the distant organ. Epithelial ovarian cancer is a heterogeneous disease that is classified into four major histological subtypes namely, serous carcinoma (SC), endometrioid carcinoma (EC), mucinous carcinoma (MC), and clear cell carcinoma (CCC). Ovarian cancer treatment is complicated due to the heterogeneity of the tumors. Patients with different subtypes respond differently to the same treatment and also have different prognoses. This diversity extends to various clinical outcomes of the disease. Thus, identifying new reliable potential biomarkers irrespective of their subtypes is an urgent need for the diagnosis and prognosis of epithelial ovarian cancer. In this study, we performed comparative gene expression analysis for identifying potential biomarkers in four histological subtypes of epithelial ovarian cancer (EOC) that include serous, endometrioid, mucinous, and clear cell carcinomas. Differentially expressed genes (DEGs) between cancerous and normal tissue samples were identified by considering the criteria of absolute logarithmic fold change |log2fc|>1 and adjusted p (padj) value<0.05. Pathway enrichment analysis of the DEGs showed that pathways in cancer, PI3K-AKT signaling pathway, RAP1 signaling pathway, cell cycle, cell adhesion molecules, and proteoglycans in cancer were common among the selected cancer subtypes. Further, we constructed the co-expression network of DEGs and identified 15 candidate genes. Finally, based on the survival analysis of the candidate genes, a total of nine genes namely ASPM, CDCA8, CENPM, CEP55, HMMR, RACGAP1, TPX2, UBE2C, and ZWINT with significant prognostic value was proposed as the potential biomarker.

bioinformatics↗

bioRxiv2020

Hypoxia-inducible lipid droplet-associated interacts with DGAT1 to promote lipid storage in hepatocytes

Lipid droplets (LD) are dynamic organelles that can expand and shrink, driven by fluctuations in the rate of triglyceride synthesis and degradation. Triglyceride synthesis, storage in LD, and degradation are governed by a complex set of LD-associated proteins. One of these LD-associated proteins, hypoxia-inducible lipid droplet-associated (HILPDA), was found to impair LD breakdown by inhibiting adipose triglyceride lipase. Here we characterized the physiological role and mechanism of action of HILPDA in hepatocytes. Expression of HILPDA was induced by fatty acids in several hepatoma cell lines. Hepatocyte-specific deficiency of HILPDA in mice modestly but significantly reduced hepatic triglycerides in mice with non-alcoholic fatty liver disease. Similarly, deficiency of HILPDA in mouse precision-cut liver slices and primary hepatocytes reduced lipid storage and accumulation of fluorescently-labelled fatty acids in LD, respectively, which was independent of adipose triglyceride lipase. Fluorescence microscopy showed that HILPDA partly colocalizes with LD and with the endoplasmic reticulum, is especially abundant in perinuclear areas, and mainly associates with newly added fatty acids. Real-time fluorescence live-cell imaging further revealed that HILPDA preferentially localizes to LD that are being remodelled. Mechanistically, HILPDA overexpression increased lipid storage in human hepatoma cells concomitant with an increase in DGAT activity and DGAT1 protein levels. Finally, confocal microscopy and Forster resonance energy transfer-fluorescence lifetime imaging microscopy analysis indicated that HILPDA colocalizes and physically interacts with DGAT1. Overall, our data indicate that HILPDA physically interacts with DGAT1 and increases DGAT activity. These findings suggest a novel mechanism in hepatocytes that links elevated fatty acid levels to stimulation of triglyceride synthesis and storage.

biochemistry↗

bioRxiv2020

Nuclear Plasticity Increases Susceptibility to Damage During Confined Migration

Large nuclear deformations during migration through confined spaces have been associated with nuclear membrane rupture and DNA damage. However, the stresses associated with nuclear damage remain unclear. Here, using a quasi-static plane strain finite element model, we map evolution of nuclear shape and stresses during confined migration of a cell through a deformable matrix. Plastic deformation of the nucleus observed for a cell with stiff nucleus transiting through a stiffer matrix lowered nuclear stresses, but also led to kinking of the nuclear membrane. In line with model predictions, transwell migration experiments with fibrosarcoma cells showed that while nuclear softening increased invasiveness, nuclear stiffening led to plastic deformation and higher levels of DNA damage. In addition to highlighting the advantage of nuclear softening during confined migration, our results suggest that plastic deformations of the nucleus during transit through stiff tissues may lead to bending-induced nuclear membrane disruption and subsequent DNA damage.

biophysics↗

bioRxiv2020

Phylogenetic diversity and activity screening of cultivable actinobacteria isolated from marine sponges and associated environments from the western coast of India

Phylogenetic diversity of cultivable actinobacteria isolated from sponges (Haliclona spp.) and associated environments of intertidal zones, along the northern parts of west coast of India, were studied using 16S rRNA gene sequences. A subset of actinobacteria were screened for three activities, namely predatory behavior, antibacterial activity and enzyme inhibition. We recovered 237 isolates of actinobacteria belonging to 19 families and 28 genera, which could be attributed to 95 putative species using maximum likelihood partition and 100 putative species using Bayesian partition in Poisson Tree Processes. Although the trends in the discovery of actinobacterial genera isolated from sponges was consistent with previous studies from different study areas, we provide first report of nine actinobacterial species from sponges. We observed widespread non-obligate epibiotic predatory behavior in eight actinobacterial genera and we provide first report of predatory activity in Brevibacterium, Glutamicibacter, Micromonospora, Nocardiopsis, Rhodococcus and Rothia. Sponge associated actinobacteria showed significantly more predatory behavior than environmental isolates. While antibacterial activity by actinobacterial isolates mainly affected Gram-positive target bacteria with little to no effect on Gram-negative bacteria, predation targeted both Gram-positive and Gram-negative prey with equal propensity. Actinobacterial isolates from both sponge and associated environment produced inhibitors of serine proteases and angiotensin converting enzyme. Predatory behavior was strongly associated with inhibition of trypsin and chymotrypsin. Our study suggests that sponge and associated environment of western coast of India are rich in actinobacterial diversity with widespread predatory activity, antibacterial activity and production of enzyme inhibitors. Understanding diversity and associations among various actinobacterial activities, with each other and the source of isolation, can provide new insights in marine microbial ecology and provide opportunities to isolate novel therapeutic agents.

microbiology↗

bioRxiv2019

A multi-dimensional, time-lapse, high content screening platform for the flatworm pathogen causing schistosomiasis

Approximately 10% of the worlds population is at risk of schistosomiasis, a disease of poverty caused by the Schistosoma parasite. To facilitate drug discovery for this complex flatworm, we developed an automated high-content screen to quantify the multidimensional responses of Schistosoma mansoni post-infective larvae (somules) to chemical insult. We describe an integrated platform to process worms at scale, collect time-lapsed, bright-field images, segment highly variable and touching worms, and then store, visualize, and query dynamic phenotypes. To demonstrate the methodology, we treated somules with seven drugs that generated diverse responses and evaluated 45 static and kinetic response descriptors relative to concentration and time. For compound screening, we used the Mahalanobis distance to compare multidimensional phenotypic effects induced by 1,323 approved drugs. Overall, we characterize both known anti-schistosomals and identify new bioactives. Apart from facilitating drug discovery, the multidimensional quantification provided by this platform will allow mapping of chemistry to phenotype.

microbiology↗

bioRxiv2019

Multivariant HIV-1 infection in infants is associated with the induction of broadly neutralizing plasma antibodies.

Due to the extensive antigenic diversity of human immunodeficiency virus-1 (HIV-1), broadly neutralizing antibodies (bnAbs) develop in a subset of infected individuals over 2-3 years of infection. Interestingly, infected infants have been shown to develop plasma bnAbs frequently and as early as one-year post-infection, with features atypical than adult bnAbs, suggesting that the factors governing bnAb induction in infants are distinct from that in adults. Understanding the viral characteristics in infected infants with early bnAb responses will provide key information on the antigenic triggers driving B cell maturation pathways towards the induction of bnAbs. Herein, we evaluated the presence of plasma bnAbs in a cohort of 51 HIV-1 clade C perinatally infected infants of Indian origin and identified viral factors associated with early bnAb responses. Plasma bnAbs targeting V2-apex on the env were predominant in infant elite and broad neutralizers. Circulating viral variants in infant elite neutralizers were susceptible to known bnAbs against V2-apex while varied resistance profile to other bnAb classes was observed. In infant elite neutralizers, multivariant infection was associated with plasma bnAbs targeting diverse autologous viruses. Our data provides information supportive of polyvalent vaccination approaches capable of inducing V2-apex bnAbs against HIV-1.

immunology↗

bioRxiv2019

Inhibiting the prostaglandin transporter PGT induces non-canonical thermogenesis at thermoneutrality

ABSTRACT / SUMMARYProstaglandins play fundamental roles in adipose tissue function. While prostaglandin F2 inhibits adipogenesis, prostaglandin E2 promotes adipose beiging. PGF2 and PGE2 are both inactivated through uptake by the plasma membrane transporter (PGT). We hypothesized that inhibiting PGT would increase PGF2 and PGE2 levels, thereby reducing white fat expansion and inducing beiging. Consistent with this hypothesis, inhibiting PGT in mice on high fat diet via genetic knockout or pharmacological blockade reduced body fat stores and induced thermogenesis at thermoneutrality. Inguinal white adipose tissue (iWAT) of these mice exhibited robust UCP1-independent thermogenesis characterized by mitochondrial expansion, coupling of O2 consumption to ATP synthesis, and induction of the creatine pathway. Enhanced coupled respiration persisted in PGT-KO iWAT adipocytes in a creatine shuttle-dependent manner. Thus, inhibiting PGT increases mitochondrial biogenesis and coupled respiration--each supported by the creatine pathway in a system lacking UCP1 expression--revealing PGT as a promising drug target against obesity.

physiology↗

bioRxiv2019

Schema: A general framework for integrating heterogeneous single-cell modalities

A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene/protein expression. Until recently, single-cell profiling experiments could measure only a single modality, leading to analysis focused on integrating information across separate experiments. However, researchers can now measure multiple modalities simultaneously in a single experiment, providing a new data paradigm that enables biological discovery but also requires new conceptual and analytic models. We therefore present Schema, an algorithm that leverages a principled metric learning strategy to synthesize multimodal information from the same experiment. To demonstrate the flexibility and power of our approach, we use Schema to infer cell types by integrating gene expression and chromatin accessibility data, perform differential gene expression analysis while accounting for batch effects and developmental age, estimate evolutionary pressure on peptide sequences, and synthesize spliced and unspliced mRNA data to infer cell differentiation. Schema can synthesize arbitrarily many modalities and capture sophisticated relationships between them, is computationally efficient, and provides a valuable conceptual model for exploring and understanding complex biology.

bioinformatics↗

bioRxiv2019

Single cell and single nucleus RNA-Seq reveal cellular heterogeneity and homeostatic regulatory networks in adult mouse stria vascularis

The stria vascularis (SV) generates the endocochlear potential (EP) in the inner ear and is necessary for proper hair cell mechanotransduction and hearing. While channels belonging to SV cell types are known to play crucial roles in EP generation, relatively little is known about gene regulatory networks that underlie the ability of the SV to generate and maintain the EP. Using single cell and single nucleus RNA-sequencing, we identify and validate known and rare cell populations in the SV. Furthermore, we establish a basis for understanding molecular mechanisms underlying SV function by identifying potential gene regulatory networks as well as druggable gene targets. Finally, we associate known deafness genes with adult SV cell types. This work establishes a basis for dissecting the genetic mechanisms underlying the role of the SV in hearing and will serve as a basis for designing therapeutic approaches to hearing loss related to SV dysfunction.

genomics↗

bioRxiv2019

A Fyn - specific biosensor reveals localized, pulsatile kinase activity and spatially regulated signaling crosstalk

Cell behavior is controlled through spatio-temporally localized protein activity. Despite unique and often contradictory roles played by Src-family-kinases (SFKs) in regulating cell physiology, activity patterns of individual SFKs have remained elusive. Here, we report a biosensor for specifically visualizing active conformation of SFK-Fyn in live cells. We deployed combinatorial library screening to isolate a binding-protein (F29) targeting activated Fyn. Nuclear-magnetic-resonance (NMR) analysis provides the structural basis of F29 specificity for Fyn over homologous SFKs. Using F29, we engineered a sensitive, minimally-perturbing fluorescence-resonance-energy-transfer (FRET) biosensor (FynSensor) that reveals cellular Fyn activity to be spatially localized, pulsatile and sensitive to adhesion/integrin signaling. Strikingly, growth factor stimulation further enhanced Fyn activity in pre-activated intracellular zones. However, inhibition of focal-adhesion-kinase activity not only attenuates Fyn activity, but abolishes growth-factor modulation. FynSensor imaging uncovers spatially-organized, sensitized signaling clusters, direct crosstalk between integrin and growth-factor-signaling, and clarifies how compartmentalized Src-kinase activity may drive cell fate.

cell biology↗

bioRxiv2019

CpG-creating Mutations are Costly in Many Human Viruses

Mutations can occur throughout the virus genome and may be beneficial or deleterious. We are interested in mutations that yield a C next to a G, producing CpG sites. CpG sites are rare in eukaryotic and viral genomes. For the eukaryotes, it is thought that CpG sites are rare because they are prone to mutation when methylated. In viruses, we know less about why CpG sites are rare. A previous study in HIV suggested that CpG-creating transition mutations are more costly that similar non-CpG-creating mutations. To determine if this is the case in other viruses, we analyzed the allele frequencies of CpG-creating and non-CpG-creating mutations across various strains, subtypes, and genes of viruses using existing data obtained from Genbank, HIV Databases, and Virus Pathogen Resource. Our results suggest that CpG sites are costly for most viruses. By understanding the cost of CpG sites, we can obtain further insights into the evolution and adaptation of viruses.

evolutionary biology↗

bioRxiv2019

Jointly embedding multiple single-cell omics measurements

Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MAs weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data.

bioinformatics↗

bioRxiv2019

Genetic image-processing using regularized selection indices

High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYTs (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by canonical SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.

genetics↗