Biology subjects

Jain, A.

Publications and source records attributed to Jain, A..

At least 145 records · Page 8Linked to original sources

bioRxiv2022

hERG1 channel subunit composition mediates proton inhibition of IKr in hiPSC CMs

hERG1 conducts cardiac IKr and is critical for repolarization of the human heart. Reduced IKr causes long QT syndrome and increases the risk for cardiac arrhythmia and sudden cardiac death. At least two subunits combine to form functional hERG1 channels, hERG1a and hERG1b. Changes in hERG 1a/1b subunit abundance modulates IKr kinetics, magnitude, and drug sensitivity. Studies from native cardiac tissue have suggested that hERG1 subunit abundance is dynamically regulated, but the impact of altered subunit abundance on IKr and its response to external stressors is not well understood. Here, we used a substrate-driven hiPSC-CM maturation model to investigate how changes in relative hERG 1a/1b subunit abundance impact the response of native IKr to extracellular acidosis, a known component of ischemic heart disease and sudden infant death syndrome. IKr recorded from immature hiPSC-CMs display a two-fold greater inhibition by extracellular acidosis (pH 6.3) compared to matured hiPSC-CMs. qRT-PCR and immunocytochemistry demonstrated that hERG1a subunit mRNA and protein were upregulated, and hERG1b subunit mRNA and protein were downregulated in matured hiPSC-CMs compared to immature hiPSC-CMs. The shift in subunit abundance in matured hiPSC-CMs was accompanied by an increased in IKr density. Silencing the impact of hERG1b on native IKr kinetics by overexpressing a polypeptide identical to the hERG1a PAS domain reduced the magnitude of IKr proton inhibition in immature hiPSC-CMs to levels comparable to those observed in matured hiPSC-CMs. These data demonstrate that hERG1 subunit abundance is dynamically regulated and that hERG1 subunit abundance determines IKr sensitivity to protons in hiPSC-CMs.

physiology↗

bioRxiv2022

KCNH2 encodes a nuclear-targeted polypeptide that mediates hERG1 channel gating and expression

KCNH2 encodes hERG1, the voltage-gated potassium channel that conducts the rapid delayed rectifier potassium current (IKr) in human cardiac tissue. hERG1 is one of the first channels expressed during early cardiac development, and its dysfunction is associated with intrauterine fetal death, sudden infant death syndrome, cardiac arrhythmia, and sudden cardiac death. Here, we identified a novel hERG1 polypeptide (hERG1NP) that is targeted to the nuclei of immature cardiac cells, including hiPSC-CMs and neonatal rat cardiomyocytes. The nuclear hERG1NP immunofluorescent signal is diminished in matured hiPSC-CMs and absent from adult rat cardiomyocytes. Antibodies targeting distinct hERG1 channel epitopes demonstrated that the hERG1NP signal maps to the hERG1 distal C-terminal domain. KCNH2 deletion using CRISPR simultaneously abolished IKr and the hERG1NP signal in hiPSC-CMs. We then identified a putative nuclear localization sequence (NLS) within the distal hERG1 C-terminus, 883-RQRKRKLSFR-892. Interestingly, the distal C-terminal domain was targeted almost exclusively to the nuclei when overexpressed HEK293 cells. Conversely, deleting the NLS from the distal peptide abolished nuclear targeting. Similarly, blocking or {beta}1 karyopherin activity diminished nuclear targeting. Finally, overexpressing the putative hERG1NP peptide in the nuclei of HEK cells significantly reduced hERG1a current density, compared to cells expressing the NLS-deficient hERG1NP or GFP. These data identify a developmentally regulated polypeptide encoded by KCNH2, hERG1NP, whose presence in the nucleus indirectly modulates hERG1 current magnitude and kinetics.

cell biology↗

bioRxiv2022

A truncating mutation of Magel2 in the rat modelled for the study of Schaaf-Yang and Prader-Willi syndromes alters select behavioral and physiological outcomes

Truncating mutations of the maternally imprinted, paternally expressed MAGEL2 gene are the predicted genetic cause of several rare neurodevelopmental disorders including Schaaf-Yang (SYS), Chitayat-Hall and Opitz Trigonocephaly C syndromes. MAGEL2 is also deleted or inactivated in Prader-Willi syndrome (PWS). Previous studies in mice have utilized Magel2 gene deletion models to examine the consequences of its absence. In this study, we report the generation, molecular validation, and phenotypic characterization of a novel rat model with a truncating Magel2 mutation generating a mutant peptide sequence more closely modeling variants associated with SYS-causing mutations. Within the hypothalamus, a brain region wherein mouse and human MAGEL2 is paternally-expressed, we demonstrate at the level of transcript and peptide detection that Magel2 in the rat exhibits a paternal, parent-of-origin effect. In the evaluation of behavioral features across several domains, juvenile Magel2 mutant rats display select alterations in anxiety-like behavior and sociability measures. Moreover, the analysis of peripheral organ systems detected alterations in body composition, cardiac structure and function, and breathing irregularities in Magel2 mutant rats. Several of these findings are concordant with reported mouse phenotypes, signifying the conservation of MAGEL2 function across rodent species for specific behavioral outcome measures. We conclude that our comprehensive analysis demonstrating impairments across multiple domains demonstrates the tractability of this model system for the study of truncating MAGEL2 mutations.

neuroscience↗

bioRxiv2022

Electric Field Responsive Nanotransducers for Glioblastoma

Electric field therapies such as Tumor Treating Fields (TTFields) have emerged as a bioelectronic treatment for isocitrate dehydrogenase wild-type and IDH mutant grade 4 astrocytoma Glioblastoma (GBM). TTFields rely on alternating current (AC) electric fields (EF) leading to the disruption of dipole alignment and induced dielectrophoresis during cytokinesis. Although TTFields have a favourable side effect profile, particularly compared to cytotoxic chemotherapy, survival benefits remain limited ([~] 4.9 months) after an extensive treatment regime (20 hours/day for 18 months). The cost of the technology also limits its clinical adoption worldwide. Therefore, the discovery of new technology that can enhance survival benefit and improve the cost per added quality of life year per patient, of these TTFields will be of great benefit to cancer treatment and decrease healthcare costs worldwide. In this work, we report the role of electrically conductive gold (GNPs), dielectric silica oxide (SiO2), and semiconductor zinc oxide (ZnO) nanoparticles (NPs) as transducers for enhancing EF mediated anticancer effects on patient derived GBM cells. Physicochemical properties of these NPs were analyzed using spectroscopic, electron microscopy, and light-scattering techniques. In vitro TTFields studies indicated an enhanced reduction in the metabolic activity of patient-derived Glioma INvasive marginal (GIN 28) and Glioma contrast enhanced core (GCE 28) GBM cells in groups treated with NPs vs. control groups, irrespective of NPs dielectric properties. Our results indicate the inorganic NPs used in this work enhance the intracellular EF effects by virtue of bipolar dielectrophoretic and electrophoretic effects. This work presents preliminary evidence which could help to improve future EF applications for bioelectronic medicine. Furthermore, the merits of spherical morphology, excellent colloidal stability, and low toxicity, make these NPs ideal for future studies for elucidating the detailed mechanism and efficacy upon their delivery in GBM preclinical models.

cancer biology↗

bioRxiv2022

The cynosure of CtBP: evolution of a bilaterian transcriptional corepressor

Evolution of sequence-specific transcription factors clearly drives lineage-specific innovations, but less is known about how changes in the central transcriptional machinery may contribute to evolutionary transformations. In particular, transcriptional regulators are rich in intrinsically disordered regions that appear to be magnets for evolutionary innovation. The C-terminal Binding Protein (CtBP) is a transcriptional corepressor derived from an ancestral lineage of alpha hydroxyacid dehydrogenases; it is found in mammals and invertebrates, and features a core NAD-binding domain as well as an unstructured C-terminus (CTD) of unknown function. CtBP can act on promoters and enhancers to repress transcription through chromatin-linked mechanisms. Our comparative phylogenetic study shows that CtBP is a bilaterian innovation whose CTD of about 100 residues is present in almost all orthologs. CtBP CTDs contain conserved blocks of residues and retain a predicted disordered property, despite having variations in the primary sequence. Interestingly, the structure of the C-terminus has undergone radical transformation independently in certain lineages including flatworms and nematodes. Also contributing to CTD diversity is the production of myriad alternative RNA splicing products, including the production of "short" tailless forms of CtBP in Drosophila. Additional diversity stems from multiple gene duplications in vertebrates, where up to five CtBP orthologs have been observed. Vertebrate lineages show fewer major modifications in the unstructured CTD, possibly because gene regulatory constraints of the vertebrate body plan place specific constraints on this domain. Our study highlights the rich regulatory potential of this previously unstudied domain of a central transcriptional regulator.

evolutionary biology↗

bioRxiv2022

Conservation at the uterine-placental interface

The hemochorial placentation site is characterized by a dynamic interplay between trophoblast cells and maternal cells. These cells cooperate to establish an interface required for nutrient delivery to promote fetal growth. In the human, trophoblast cells penetrate deep into the uterus. This is not a consistent feature of hemochorial placentation and has hindered the establishment of suitable animal models. The rat represents an intriguing model for investigating hemochorial placentation with deep trophoblast cell invasion. In this study, we used single cell RNA sequencing to characterize the transcriptome of the invasive trophoblast cell lineage, as well as other cell populations within the rat uterine-placental interface during early (gestation day, gd, 15.5) and late (gd 19.5) stages of intrauterine trophoblast cell invasion. We identified a robust set of transcripts that define invasive trophoblast cells, as well as transcripts that distinguished endothelial, smooth muscle, natural killer, and macrophage cells. Invasive trophoblast, immune, and endothelial cell populations exhibited distinct spatial relationships within the uterine-placental interface. Furthermore, the maturation stage of invasive trophoblast cell development could be determined by assessing gestation-stage dependent changes in transcript expression. Finally, and most importantly, expression of a prominent subset of rat invasive trophoblast cell transcripts is conserved in the invasive extravillous trophoblast cell lineage of the human placenta. These findings provide foundational data to identify and interrogate key conserved regulatory mechanisms essential for development and function of an important compartment within the hemochorial placentation site that is essential for a healthy pregnancy. SIGNIFICANCETrophoblast cell-guided restructuring of the uterus is an essential event in the establishment of the hemochorial placenta. Establishment of a suitable animal model for investigating regulatory mechanisms in this critical developmental process is a key to better understanding the etiology of diseases of placentation, such as early pregnancy loss, preeclampsia, intrauterine growth restriction, and preterm birth. The rat exhibits deep trophoblast cell invasion, as seen in human hemochorial placentation. Similarities are identified in the transcriptomes of rat and human invasive trophoblast cells, leading to the discovery of conserved candidate regulators of the invasive trophoblast cell lineage. This creates opportunities to test hypotheses underlying the pathophysiologic basis of trophoblast cell-guided uterine transformation and new insights into the etiology of diseases of placentation.

developmental biology↗

bioRxiv2022

Allosteric inactivation of an engineered optogenetic GTPase

Optogenetics is a technique for establishing direct spatiotemporal control over molecular function within living cells using light. Light application induces conformational changes within targeted proteins that produce changes in function. One of the applications of optogenetic tools is an allosteric control of proteins via light-sensitive LOV2 domain, which allows direct and robust control of protein function. Computational studies supported by cellular imaging demonstrated that application of light allosterically controlled signaling proteins Vav2, ITSN, and Rac1, but the structural and dynamic basis of such control has yet to be elucidated by experiment. Here, using NMR spectroscopy, we discover principles of action of allosteric control of cell division control protein 42 (CDC42), a small GTPase involved in cell signaling. Both LOV2 and Cdc42 employ flexibility in their function to switch between "dark"/ "lit" or active/inactive states, respectively. By conjoining Cdc42 and LOV2 domains into the bi-switchable fusion Cdc42Lov, application of light - or alternatively, mutation in LOV2 to mimic light absorption - allosterically inhibits Cdc42 downstream signaling. The flow and patterning of allosteric transduction in this flexible system is well-suited to observation by NMR. Close monitoring of the structural and dynamic properties of dark versus lit states of Cdc42Lov revealed lit-induced allosteric perturbations. Chemical shift perturbations for lit mimic, I539E, have distinct regions of sensitivity and both the domains are coupled together leading to bi-directional interdomain signaling. Insights gained from this optoallosteric design will increase our ability to control response sensitivity in future designs. Significance StatementControl of cell signaling activity in proteins by light is one of the primary goals of optogenetics. The hybrid light-receptor/cell-signaling protein Cdc42Lov was engineered recently as an optogenetic tool, employing a novel allosteric strategy that results in photoinhibition. In contrast to previous activation designs, the mechanism of inhibition of GTPase signaling activity in Cdc42 is only apparent at a detailed structural and dynamic level. NMR characterization of dark and mutationally "lit" forms reveals the allosteric interdomain perturbations, knowledge of which will enhance future applications of this design strategy.

biophysics↗

bioRxiv2022

Time series analysis of SARS-CoV-2 genomes and correlations among highly prevalent mutations

The efforts of the scientific community to tame the recent SARS-CoV-2 pandemic seems to have been diluted by the emergence of new viral strains. Therefore, it becomes imperative to study and understand the effect of mutations on viral evolution, fitness and pathogenesis. In this regard, we performed a time-series analysis on 59541 SARS-CoV-2 genomic sequences from around the world. These 59541 genomes were grouped according to the months (January 2020-March 2021) based on the collection date. Meta-analysis of this data led us to identify highly significant mutations in viral genomes. Correlation and Hierarchical Clustering of the highly significant mutations led us to the identification of sixteen mutation pairs that were correlated with each other and were present in >30% of the genomes under study. Among these mutation pairs, some of the mutations have been shown to contribute towards the viral replication and fitness suggesting the possible role of other unexplored mutations in viral evolution and pathogenesis. Additionally, we employed various computational tools to investigate the effects of T85I, P323L, and Q57H mutations in Non-structural protein 2 (Nsp2), RNA-dependent RNA polymerase (RdRp) and Open reading frame 3a (ORF3a) respectively. Results show that T85I in Nsp2 and Q57H in ORF3a mutations are deleterious and destabilize the parent protein whereas P323L in RdRp is neutral and has a stabilizing effect. The normalized linear mutual information (nLMI) calculations revealed the significant residue correlation in Nsp2 and ORF3a in contrast to reduce correlation in RdRp protein.

bioinformatics↗

bioRxiv2022

Structural basis of Omicron immune evasion: A comparative computational study of Spike protein-Antibody interaction.

The COVID-19 pandemic has caused more than 424 million infections and 5.9 million deaths so far. The vaccines used against SARS-COV-2 by now have been able to develop some neutralising antibodies in the vaccinated human population and slow down the infection rate. The effectiveness of the vaccines has been challenged by the emergence of the new strains with numerous mutations in the spike (S) protein of SARS-CoV-2. Since S protein is the major immunogenic protein of the virus and also contains Receptor Binding Domain (RBD) that interacts with the human Angiotensin-Converting Enzyme 2 (ACE2) receptors, any mutations in this region should affect the neutralisation potential of the antibodies leading to the immune evasion. Several variants of concern (VOC) of the virus have emerged so far. Among them, the most critical are Delta (B.1.617.2), and recently reported Omicron (B. 1.1.529) which have acquired a lot of mutations in the spike protein. We have mapped those mutations on the modelled RBD and evaluated the binding affinities of various human antibodies with it. Docking and molecular dynamics simulation studies have been used to explore the effect of the mutations on the structure of the RBD and the RBD-antibody interaction. The analysis shows that the mutations mostly at the interface of a nearby region lower the binding affinity of the antibody by ten to forty per cent, with a downfall in the number of interactions formed as a whole and therefore, it implies the generation of immune escape variants. Notable mutations and their effect was characterised by performing various analyses that explain the structural basis of antibody efficacy in Delta and a compromised neutralisation effect for the Omicron variant. Our results pave the way for robust vaccine design that can be effective for many variants. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY SynopsisThe research study utilises comparative docking and MD simulations analyses to illustrate how mutations in delta and omicron variants affect the binding of antibodies to the spike receptor binding domain (RBD) of SARS CoV-2.

biophysics↗

bioRxiv2022

Modulation of the extracellular matrix by Streptococcus gallolyticus subsp. gallolyticus and importance in cell proliferation

Streptococcus gallolyticus subspecies gallolyticus (Sgg) has a strong clinical association with colorectal cancer (CRC) and actively promotes the development of colon tumors. Previous work showed that this organism stimulates CRC cells proliferation and tumor growth. However, the molecular mechanisms underlying these activities are not well understood. Here, we found that Sgg upregulates the expression of several types of collagens in HT29 and HCT116 cells, with type VI collagen (ColVI) being the highest upregulated collagen type. Knockdown of ColVI abolished the ability of Sgg to induce cell proliferation and reduced the adherence of Sgg to CRC cells. The extracellular matrix (ECM) is an important regulator of cell proliferation. Therefore, we further examined the role of decellularized matrix (dc-matrix), which is free of live bacteria or cells, in Sgg-induced cell proliferation. Dc-matrix prepared from Sgg-treated cells showed a significantly higher pro-proliferative activity than that from untreated cells or cells treated with the control bacteria. On the other hand, dc-matrix from Sgg-treated ColVI knockdown cells showed no difference in the capacity to support cell proliferation compared to that from untreated ColVI knockdown cells, suggesting that the ECM by itself is a mediator of Sgg-induced cell proliferation. Furthermore, Sgg-treated CRC cells formed significantly larger tumors in vivo, whereas Sgg treatment had no effect on ColVI knockdown cells, suggesting that ColVI is important for Sgg to promote tumor growth in vivo. These results highlight a dynamic bidirectional interplay between Sgg and the ECM, where Sgg upregulates collagen expression. The Sgg-modified ECM in turn affects the ability of Sgg to adhere to host cells and more importantly, acts as a mediator for Sgg-induced CRC cell proliferation. Taken together, our results reveal a novel mechanism in which Sgg stimulates CRC proliferation through modulation of the ECM. Author SummaryColorectal cancer (CRC) is a leading cause of cancer-related death. The development of CRC can be strongly influenced by specific gut microbes. Understanding how gut microbes modulate CRC is critical to developing novel strategies to improve clinical diagnosis and treatment of this disease. S. gallolyticus subsp. gallolyticus (Sgg) has a strong clinical association with CRC and actively promotes the development of colon tumors. However, the mechanisms Sgg utilizes to promote tumors are not well understood. Our results showed for the first time a dynamic interplay between Sgg and the extracellular matrix. We found that Sgg upregulates the expression of collagens which in turn affects the interaction between Sgg and CRC cells and mediates CRC cell proliferation. These findings draw attention to a previously unrecognized dynamic bidirectional interplay between a CRC-associated microbe and the extracellular matrix (ECM). Given the importance of the ECM in normal homeostasis and in tumor microenvironment, these findings have important implications in the context of microbial contribution to cancer.

microbiology↗

bioRxiv2022

Investigating the performance of deep learning methods for Hi-C resolution improvement

MotivationHi-C is a widely used technique to study the 3D organization of the genome. Due to its high sequencing cost, most of the generated datasets are of coarse resolution, which makes it impractical to study finer chromatin features such as Topologically Associating Domains (TADs) and chromatin loops. Multiple deep-learning-based methods have recently been proposed to increase the resolution of these data sets by imputing Hi-C reads (typically called upscaling). However, the existing works evaluate these methods on either synthetically downsampled or a small subset of experimentally generated sparse Hi-C datasets, making it hard to establish their generalizability in the real-world use case. We present our framework - Hi-CY - that compares existing Hi-C resolution upscaling methods on seven experimentally generated low-resolution Hi-C datasets belonging to various levels of read sparsities originating from three cell lines on a comprehensive set of evaluation metrics. Hi-CY also includes four downstream analysis tasks, such as TAD and chromatin loops recall, to provide a thorough report on the generalizability of these methods. ResultsWe observe that existing deep-learning methods fail to generalize to experimentally generated sparse Hi-C datasets showing a performance reduction of up to 57 %. As a potential solution, we find that retraining deep-learning based methods with experimentally generated Hi-C datasets improves performance by up to 31%. More importantly, Hi-CY shows that even with retraining, the existing deep-learning based methods struggle to recover biological features such as chromatin loops and TADs when provided with sparse Hi-C datasets. Our study, through Hi-CY framework, highlights the need for rigorous evaluation in future. We identify specific avenues for improvements in the current deep learning-based Hi-C upscaling methods, including but not limited to using experimentally generated datasets for training. Availabilityhttps://github.com/rsinghlab/Hi-CY Author SummaryWe evaluate deep learning-based Hi-C upscaling methods with our framework Hi-CY using seven datasets originating from three cell lines evaluated using three correlation metrics, four Hi-C similarity metrics, and four downstream analysis tasks, including TAD and chromatin loop recovery. We identify a distributional shift between Hi-C contact matrices generated from downsampled and experimentally generated sparse Hi-C datasets. We use Hi-CY to establish that the existing methods trained with downsampled Hi-C datasets tend to perform significantly worse on experimentally generated Hi-C datasets. We explore potential strategies to alleviate the drop in performance such as retraining models with experimentally generated datasets. Our results suggest that retraining improves performance up to 31 % on five sparse GM12878 datsets but provides marginal improvement in cross cell-type setting. Moreover, we observe that regardless of the training scheme, all deep-learning based methods struggle to recover biological features such as TADs and chromatin loops when provided with very sparse experimentally generated datasets as inputs.

bioinformatics↗

bioRxiv2022

Evidence for quinol oxidase activity of ImoA, a novel NapC/NirT family protein from the neutrophilic Fe(II) oxidizing bacterium Sideroxydans lithotrophicus ES-1

The freshwater chemolithoautotrophic Gram-negative bacterium Sideroxydans lithotrophicus ES-1 oxidizes Fe(II) at the cell surface. In this organism, it is proposed that the monoheme cytochrome MtoD from the Mto pathway transfer electrons across the periplasm to an inner membrane NapC/NirT family tetraheme cytochrome encoded by Slit_2495, for which we propose the name ImoA (inner membrane oxidoreductase). ImoA has been proposed to function as the quinone reductase, receiving electrons from iron oxidizing extracellular electron uptake pathway to reduce the quinone pool. In this study, ImoA was cloned on a pBAD plasmid vector and overexpressed in Escherichia coli. Biochemical and spectroscopic characterization of the purified ImoA reveals that this 26.5 kDa cytochrome contains one high-spin and three low-spin hemes. Our data show that ImoA can function as a quinol oxidase and is able to functionally replace CymA, a related NapC/NirT family tetraheme cytochrome required for anaerobic respiration of a wide range of substrates by Shewanella oneidensis. We demonstrate that ImoA can transfer electrons to different periplasmic proteins from S. oneidensis including STC and FccA, but in a manner that is distinct from that of CymA. Phylogenetic analysis shows that ImoA is clustered closer to NirT sequences than to CymA. This study suggests that ImoA functions as a quinol oxidase in S. oneidensis and raises questions about the directionality and/or reversibility of electron flow through the Mto pathway in S. lithotrophicus ES-1. ImportanceFe(II)-oxidizing bacteria play an important role in the biogeochemical cycling of iron, representing a promising class of organisms for the development of novel biotechnological processes, including bioelectrosynthesis. These organisms perform extracellular electron transfer, taking up electrons from Fe(II) outside of the cell, possibly through a porin-cytochrome complex in the outer membrane. The electrons are then transferred to the quinone pool in the inner membrane via periplasmic and inner membrane electron transfer proteins. In this paper, we produced and characterized the NapC/NirT family tetraheme cytochrome ImoA, encoded by Slit_2495, an inner membrane protein from the Gram-negative Fe(II)-oxidizing bacterium Sideroxydans lithotrophicus ES-1, proposed to be involved in extracellular electron transfer to the quinone pool. We show that ImoA may function instead as a quinol oxidase. The obtained insights represent the first step in understanding mechanisms of electron flow in S. lithotrophicus ES-1 and may lead towards practical biotechnological applications of Fe(II)-oxidizing bacteria.

biochemistry↗

bioRxiv2022

A visual atlas of genes tissue-specific pathological roles

Dysregulation of a genes function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape (HCL) project [1], researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite heterogeneous and is confounded by several clinical and demographic covariates. To circumvent this, we mined ~18 million PubMed abstracts published till May 2019 and selected ~6.1 million of them that describe the pathological role of genes in different diseases. Further, we employed a word embedding technique from the domain of Natural Language Processing (NLP) to learn vector representation of entities such as genes, diseases, tissues, etc., in a way such that their relationship is preserved in a vector space. Notably, Pathomap, by the virtue of its underpinning theory, also learns transitive relationships. Pathomap provided a vector representation of words indicating a possible association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS). The first manuscript reporting this finding was not part of our training data. Key pointsO_LIWe mined ~18 million PubMed abstracts to extract latent knowledge pertaining to tissue specific pathological roles of genes. C_LIO_LIWe found well-defined gene modules implicated in disease pathogenesis in anatomically proximal tissues. C_LIO_LIWe demonstrated an ahead of time discovery of the association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS), as a knowledge synthesis use-case. C_LI

bioinformatics↗

bioRxiv2021

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

The Nile rat (Avicanthis niloticus) is an important animal model for biomedical research, including the study of diurnal rhythms and type 2 diabetes. Here, we report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3,613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including those that affect genes associated with type 2 diabetes and metabolic dysfunctions. These include 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse. Our findings reflect the exceptional level of genomic detail present in this assembly, which will greatly expand the potential of the Nile rat as a model organism for genetic studies.

genomics↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗

bioRxiv2021

ICOR: Improving codon optimization with recurrent neural networks

BackgroundIn protein sequences--as there are 61 sense codons but only 20 standard amino acids-- most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. ResultsWe demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICORs performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution. ConclusionsThe results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

synthetic biology↗

bioRxiv2021

Saturated Proteostasis and Nuclear Injuries Defeat Homeostatic Potentials of α-Synuclein Filaments

Biogenesis of inclusion bodies (IBs) facilitates protein quality control (PQC). Canonical aggresomes execute degradation of misfolded proteins while non-degradable amyloids quarantine into Insoluble Protein Deposits. Lewy Bodies (LBs) are well-known neurodegenerative IBs of -Synuclein associated diseases like Dementia with Lewy bodies (DLB) and Parkinsons disease. Intriguingly, the PQC benefits and drawbacks associated with LBs remain underexplored. Here, we report that the crosstalk between LBs and aggresome-like IBs of -Synuclein (Syn-aggresomes) buffers amyloidogenic -Synuclein load in primary neurons and mitotic cell models. Using cellular biochemistry, genetic knockdown, and microscopy tools we find that LBs possess unorthodox PQC-capacities of self-quarantining the Syn-amyloids, and being degradable upon receding fresh amyloidogenesis. Syn-aggresomes equilibrate biogenesis of LBs by facilitating spontaneous and opportunistic turnover of soluble -Synuclein and Syn-amyloids, respectively. However, LBs overgrow at the perinucleus once amyloidogenesis sets in and are misidentified by cytosolic BICD2 as cargos for motor-protein dynein. Simultaneously, microtubules surrounding the perinuclear LBs are distorted, misbalancing the dynein motor-force on nucleoskeleton leading to lamina injuries. Like typical Laminopathies, nucleocytoplasmic mixing, DNA-damage, and deregulated transcription of stress chaperones defeat the proteostatic purposes of LBs. We confirmed the lamina disintegrities in brain sections of Parkinsons disease patients. Together, our study provides insights into the intricate complexities of proteostatic possibilities associated with -Synuclein inclusions and offers understanding on the proteostasis-sensitivity of LB-containing aging neurons via lamina-injuries. Significance statementAmyloid inclusions of -Synuclein called Lewy Bodies (LBs) are hallmark of multiple neurodegenerative diseases like Dementia with Lewy Bodies and Parkinsons disease. A significant reason for the degeneration of LB-containing aging neurons in these diseases is their sensitivity to proteostasis stresses. We find two distinct inclusions of -Synuclein in the same neurons/cells. First, the Syn-aggresomes titrate the biogenesis of the other i.e., the LBs, by facilitating degradation of soluble -Synuclein. Syn-amyloids deposited in LBs remain degradable but LBs overgrow at the perinucleus once the kinetics of amyloid-biogenesis exceeds aggresome-assisted degradation of -Synuclein. Perinuclear LBs destabilize local tubulin-meshwork and are misidentified as cargo for cytosolic motor dynein. Aberrant cytoskeleton-nucleoskeleton tension disintegrates lamina, deregulates stress-responsive transcription, and triggers proteostasis-sensitivity in LB-containing neurons.

cell biology↗

bioRxiv2021

Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning

Quantitative systems pharmacology (QsP) may need to change in order to accommodate machine learning (ML), but ML may need to change to work for QsP. Here we investigate the use of neural network surrogates of stiff QsP models. This technique reduces and accelerates QsP models by training ML approximations on simulations. We describe how common neural network methodologies, such as residual neural networks, recurrent neural networks, and physics/biologically-informed neural networks, are fundamentally related to explicit solvers of ordinary differential equations (ODEs). Similar to how explicit ODE solvers are unstable on stiff QsP models, we demonstrate how these ML architectures see similar training instabilities. To address this issue, we showcase methods from scientific machine learning (SciML) which combine techniques from mechanistic modeling with traditional deep learning. We describe the continuous-time echo state network (CTESN) as the implicit analogue of ML architectures and showcase its ability to accurately train and predict on these stiff models where other methods fail. We demonstrate the CTESNs ability to surrogatize a production QsP model, a >1,000 ODE chemical reaction system from the SBML Biomodels repository, and a reaction-diffusion partial differential equation. We showcase the ability to accelerate QsP simulations by up to 56x against the optimized DifferentialEquations.jl solvers while achieving <5% relative error in all of the examples. This shows how incorporating the numerical properties of QsP methods into ML can improve the intersection, and thus presents a potential method for accelerating repeated calculations such as global sensitivity analysis and virtual populations.

pharmacology and toxicology↗