Biology subjects

Mehra, R.

Publications and source records attributed to Mehra, R..

18 recordsLinked to original sources

bioRxiv2026

Solvent-buffer effects in molecular dynamics simulations of nucleic acids

Molecular dynamics simulations of nucleic acids are performed using a solvent-buffer distance of 10 [A] between the solute surface and the simulation box boundary. Although this cell size has been extensively explored in protein simulations, its implications for nucleic acid dynamics are not well understood. Nucleic acids are elongated, highly charged, and flexible structures with hydration and dynamical properties distinct from those of proteins and therefore, they may require different solvent-layer considerations in simulations. In this study, we investigated the effect of simulation cell size on nucleic acid dynamics by simulating a 30-base-pair double-helical nucleic acid structure and its two single-stranded forms using solvent-buffer distances of 3, 5, 10, 15, and 20 [A]. Smaller cells may impose restricted hydration, molecular crowding, and periodic image interactions. However, larger cells provide solvent space for conformational relaxation. A total of 45 {micro}s of molecular dynamics simulations were performed (3 structures x 5 cell sizes x 3 replicates x 1 {micro}s). Our results show that while the commonly used 10 [A] buffer may be sufficient to maintain the stability of the double-stranded nucleic acid, larger cells are required to capture the conformational dynamics of single-stranded structures. In both, increasing the cell size to 15 or 20 [A] enables broader conformational sampling. The first hydration shell exhibits reduced crowding in the 20 [A] cell, consistent with more relaxed conformations. At larger cell sizes, single-stranded nucleic acids adopt compact, self-associated conformations for stability. Together, this study presents physical insight into how simulation cell size and solvent environment influence nucleic acid dynamics.

biophysics↗

bioRxiv2026

NNMT Loss Drives Cancer Progression by enhancing SAM availability for mTORC1 Signaling and Chromatin Methylation

Aberrant epigenetic reprogramming together with dysregulated mTOR signaling are hallmarks of cancer, where altered chromatin methylation and nutrient-sensing pathways cooperate to drive tumor progression. S-adenosylmethionine (SAM), the universal methyl donor, is essential for these processes, yet how tumors sustain elevated SAM availability to support oncogenic transmethylation reactions remains poorly defined. Here, using prostate cancer (PCa) as a model system, we identify nicotinamide N-methyltransferase (NNMT) as a critical metabolic-epigenetic regulator and tumor suppressor. Using a prostate-specific Nnmt knockout mouse model, we demonstrate that NNMT loss accelerates PCa progression, particularly in the context of Pten deletion, resulting in infiltrating carcinoma and reduced survival. Mechanistically, NNMT functions as a "SAM-sink," and its loss increases intracellular SAM abundance, thereby activating mTORC1 signaling through SAMTOR-dependent sensing and broadly enhancing chromatin methylation. In human PCa, recurrent genomic deletions of NNMT occur in up to 7% of cases, and NNMT protein expression is largely absent in primary tumors and metastases. NNMT-deficient PCa cells exhibit elevated SAM:SAH ratios, increased histone methylation, and heightened mTORC1 activity, enabling sustained tumor growth even under dietary methionine-restriction (MR). Notably, combined MR and pharmacologic mTORC1 inhibition synergistically suppresses the growth of NNMT-deficient tumors, revealing a previously unrecognized therapeutic vulnerability. Collectively, these findings establish NNMT as a key tumor suppressor that constrains SAM-driven epigenetic and signaling programs in PCa and suggest a rational, diet-based therapeutic strategy for advanced cancers with NNMT loss.

cancer biology↗

bioRxiv2025

Enhancing RNA 3D Folding Prediction via Transformer and Equivariant Architectures under Resource Constraints

Predicting the three-dimensional structure of RNA molecules is a fundamental problem in computational biology, with applications in understanding gene regulation, viral function, and drug design. Existing RNA structure prediction methods often rely on large models or heavy computation, as exemplified by protein folding solutions like AlphaFold2 [2], which makes them impractical under tight resource constraints. In this work, we present an efficient neural architecture that integrates a Transformer encoder enhanced with Rotary Positional Embeddings (RoPE) and a small residual MLP block, along with an optional E(n)-equivariant refinement module. We train our model on the Stanford Ribonanza RNA 3D dataset [11] using BF16 precision on a single NVIDIA H100 (80 GB) GPU. Our model achieves lower RMSD than simpler baselines, demonstrating that modern Transformer and equivariant techniques can be effectively applied to RNA folding under practical compute constraints.

bioinformatics↗

bioRxiv2025

Spatial analysis reveals a novel inflammatory tumor transition state which promotes a macrophage-driven induction of sarcomatoid renal cell carcinoma

Sarcomatoid renal cell carcinoma (sRCC) is an aggressive transdifferentiation of epithelioid clear cell RCC (ccRCC) tumors that shows heightened response to immunotherapy. The underlying biology leading to sarcomatoid transformation and mechanisms contributing to immunotherapy response are not well understood. Novel single cell spatial techniques were used in ccRCC and sRCC tumors from 40 patients to understand the spatial sRCC transformation and corresponding immune changes. A transcriptional transition state in epithelioid ccRCC cells along a continuum to mesenchymal sRCC was identified which expresses high levels of pro-inflammatory cytokines and an immune infiltrate. In vitro studies demonstrated that M2-like macrophages, recruited to the tumor by the transition state, induce full transition to the sarcomatoid state. A combination of increased PD-L1 expression and T-cells recruited by the transition state was observed, consistent with the increased immunotherapy response. This study enriches our understanding of the mechanisms leading to development and immune responsiveness of sRCC paving the way for novel approaches to diminish RCC progression.

cancer biology↗

bioRxiv2025

Distinct substrate and intermediate recognition via mutation effects on Mycobacterium tuberculosis methionyl-tRNA synthetase

Tuberculosis kills millions worldwide. Drug-resistance demands exploring new targets against this illness. Methionyl-tRNA synthetase (MetRS) is a crucial target in Mycobacterium tuberculosis (Mtb) that participates in initiation and elongation of translation and represents a protein of evolutionary interest. To elucidate the structure-function relationships of MetRS, we performed detailed sequence analyses and molecular dynamics simulations of Mtb MetRS in the substrate- bound (methionine and ATP) and intermediate (methionyl-AMP) states, for both the wild-type and three single-mutant forms (H21A, K54A, and E130A). Eight systems (two wild-type and six mutants) were simulated for 24 microseconds. Differential dynamics and binding effects of the substrate versus intermediate states were identified, along with the molecular reasons for the loss of activity in mutants. The wild-type substrate state was more stable than the intermediate state. In contrast, the mutants were more unstable in the substrate state, but incorporated stability into the intermediate state protein. These findings suggest that methionyl-AMP, being a reaction intermediate, exhibits a short residence time at the proteins active site, while the substrate state shows a longer residence time of methionine and ATP. The increased instability of mutants in the substrate state indicates disruption of the pyrophosphate-ATP exchange by altering substrate- protein interactions. Once the intermediate is formed, the mutations have minimal or no effect. These observations are consistent with experimental data. In brief, our study finds the molecular basis for the distinct substrate and intermediate recognition by Mtb MetRS and establishes a mechanism for loss of activity in the mutants.

biophysics↗

bioRxiv2025

Initiator and Elongator tRNA Recognition Mechanism in Mycobacterium tuberculosis Methionyl-tRNA Synthetase

The protein synthesis is an essential target for anti-tubercular drug design. Methionyl-tRNA synthetase (MetRS) typically plays a role in elongation and the initiation of protein synthesis. Molecular recognition of the CAU anticodon of tRNA by MetRS in the two processes is a crucial step. Until now, no known experimental structures for Mycobacterium tuberculosis (Mtb) show this binding. We therefore modeled the Mtb MetRS complexes with initiator and elongator tRNAs to find their differential binding mechanism during molecular dynamics simulations of 6 {micro}s. We found that the elongator tRNA binding was stable with the protein, while the initiator tRNA binds transiently, with major intra-tRNA interactions maintained in both. This could be due to fast initiator tRNA charging in contrast to the elongator tRNA, which could take more time to charge. tRNA interacts with the MetRS active site and anticodon domain. The electrostatic attractions between tRNA and the proteins catalytic domain possibly caused its charging with methionine. The repulsive and attractive forces between tRNA and the proteins connective peptide domain and KMSKS loop triggered the opening of the binding pocket, causing the reaction and the product release. At the same time, tRNAs strong binding to the proteins anticodon domain facilitated this reaction. These events show the possible pathway of tRNA charging. tRNA formed salt-bridges with the positively charged Arg and Lys, whereas the negatively charged Asp and Glu caused repulsive binding. In brief, this study provided a plausible mechanism for initiator versus elongator tRNA recognition by Mtb MetRS.

biophysics↗

bioRxiv2025

Computing pathogenicity of mutations in human cytochrome P450 superfamily

Cytochrome P450 (CYP) are heme-containing enzymes involved in the metabolism of drugs and endogenous compounds. Humans have 57 known CYPs, with >200 mutations linked to severe disorders, highlighting their role in disease etiology. To investigate their pathogenicity, we performed an in-depth computational study, comparing pathogenic mutations with non-pathogenic ones. First, we evaluated the effects of all possible mutations across 26 known CYPs structures using five structure-and five sequence-based methods, aiming to reproduce pathogenesis. We computed 23,46,410 mutations, categorized into: all possible, non-pathogenic, and pathogenic. Comparisons consistently revealed a meaningful stability pattern: non-pathogenic > all > pathogenic. Second, we found that a significantly higher number of pathogenic mutations were buried in CYP structures, relating to their higher pathogenesis potential. Third, we analyzed mutation allele frequency relative to solvent accessibility. Fourth, we computed change in 48 amino acid properties upon mutation and identified three distinguishing features: Gibbs free energy, isoelectric point and volume. Fifth, the positive residue content was reduced significantly in diseased mutations, with arginine mutations being the main culprit, directly linked to isoelectric point change. Sixth, we found a higher propensity for pathogenic mutations in conserved sites, suggesting disruption of CYP function. Finally, analysis of heme versus substrate sites showed a higher frequency of pathogenic mutations in heme site, with arginine being the main mutating residue, possibly disrupting arginine-heme interactions. We provide the first comprehensive analysis of mutation effects across multiple CYPs. We model the chemical basis of CYP-related pathogenicity, paving way for a semi-quantitative model to predict diseases.

biophysics↗

bioRxiv2024

UniGEN-DDI: Computing drug-drug interactions using a unified graph embedding network

Finding drug-drug interaction is crucial for patient safety and treatment efficacy. Two drugs may show a synergistic effect but may sometimes cause a severe health issue, including lethality. Wet lab studies are often performed to understand such interactions but are limited by cost and time. However, the biochemical data generated can be explored to compute unknown interactions. Here, we developed a computational model named UniGEN-DDI (Unified Graph Embedding Network for Drug-Drug Interaction) for the estimation of interactions between drugs. It is a simple unified network model containing the biochemical information of drug association developed using the compiled data from DrugBank 5.1.0. The feature learning of the drugs was carried out using a combination of GraphSAGE and Node2Vec algorithms, which were found efficient in extracting diverse features. The simple architecture of our model led to a significant reduction in computational time compared to the baselines, while maintaining a high prediction accuracy. The model performed well on the data, which was equally distributed between interacting and non-interacting drugs. As a more challenging evaluation, we performed non-overlapping splitting of the data based on the drug action on different parts of the body, and our model performed well in both interaction estimation and time efficiency. Our model successfully identified the 12 unknown drug interactions in DrugBank 5.1.0, which were updated in DrugBank 6.0. HighlightsO_LIUniGEN-DDI: A simple unified network model was developed for drug-drug interactions. C_LIO_LINode embeddings were generated using GraphSAGE and Node2Vec algorithms. C_LIO_LIA significantly higher computational efficiency was achieved compared to baselines. C_LIO_LIModel validated in non-overlapping splitting of data targetting multiple body parts. C_LIO_LIUniGEN-DDI estimated unknown interactions verified in the updated DrugBank 6.0. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/615812v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@909caaorg.highwire.dtl.DTLVardef@1d7cfdorg.highwire.dtl.DTLVardef@1f8ca34org.highwire.dtl.DTLVardef@a340d6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Targeting PIKfyve-driven lipid homeostasis as a metabolic vulnerability in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) subsists in a nutrient-deregulated microenvironment, making it particularly susceptible to treatments that interfere with cancer metabolism1 2. For example, PDAC utilizes and is dependent on high levels of autophagy and other lysosomal processes3-5. Although targeting these pathways has shown potential in preclinical studies, progress has been hampered by the challenge of identifying and characterizing favorable targets for drug development6. Here, we characterize PIKfyve, a lipid kinase integral to lysosomal functioning7, as a novel and targetable vulnerability in PDAC. In human patient and murine PDAC samples, we discovered that PIKFYVE is overexpressed in PDAC cells compared to adjacent normal cells. Employing a genetically engineered mouse model, we established the essential role of PIKfyve in PDAC progression. Further, through comprehensive metabolic analyses, we found that PIKfyve inhibition obligated PDAC to upregulate de novo lipid synthesis, a relationship previously undescribed. PIKfyve inhibition triggered a distinct lipogenic gene expression and metabolic program, creating a dependency on de novo lipid metabolism pathways, by upregulating genes such as FASN and ACACA. In PDAC, the KRAS-MAPK signaling pathway is a primary driver of de novo lipid synthesis, specifically enhancing FASN and ACACA levels. Accordingly, the simultaneous targeting of PIKfyve and KRAS-MAPK resulted in the elimination of tumor burden in a syngeneic orthotopic model and tumor regression in a xenograft model of PDAC. Taken together, these studies suggest that disrupting lipid metabolism through PIKfyve inhibition induces synthetic lethality in conjunction with KRAS-MAPK-directed therapies for PDAC.

cancer biology↗

bioRxiv2024

Targeting the mSWI/SNF Complex in POU2F-POU2AF Transcription Factor-Driven Malignancies

The POU2F3-POU2AF2/3 (OCA-T1/2) transcription factor complex is the master regulator of the tuft cell lineage and tuft cell-like small cell lung cancer (SCLC). Here, we found that the POU2F3 molecular subtype of SCLC (SCLC-P) exhibits an exquisite dependence on the activity of the mammalian switch/sucrose non-fermentable (mSWI/SNF) chromatin remodeling complex. SCLC-P cell lines were sensitive to nanomolar levels of a mSWI/SNF ATPase proteolysis targeting chimera (PROTAC) degrader when compared to other molecular subtypes of SCLC. POU2F3 and its cofactors were found to interact with components of the mSWI/SNF complex. The POU2F3 transcription factor complex was evicted from chromatin upon mSWI/SNF ATPase degradation, leading to attenuation of downstream oncogenic signaling in SCLC-P cells. A novel, orally bioavailable mSWI/SNF ATPase PROTAC degrader, AU-24118, demonstrated preferential efficacy in the SCLC-P relative to the SCLC-A subtype and significantly decreased tumor growth in preclinical models. AU-24118 did not alter normal tuft cell numbers in lung or colon, nor did it exhibit toxicity in mice. B cell malignancies which displayed a dependency on the POU2F1/2 cofactor, POU2AF1 (OCA-B), were also remarkably sensitive to mSWI/SNF ATPase degradation. Mechanistically, mSWI/SNF ATPase degrader treatment in multiple myeloma cells compacted chromatin, dislodged POU2AF1 and IRF4, and decreased IRF4 signaling. In a POU2AF1-dependent, disseminated murine model of multiple myeloma, AU-24118 enhanced survival compared to pomalidomide, an approved treatment for multiple myeloma. Taken together, our studies suggest that POU2F-POU2AF-driven malignancies have an intrinsic dependence on the mSWI/SNF complex, representing a therapeutic vulnerability.

cancer biology↗

bioRxiv2023

Computational fitness estimates of SARS-CoV-2 mutations affecting spike protein binding to antibodies and ACE2

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogen. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein ([~]3,80,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

bioinformatics↗

bioRxiv2022

Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding

The binding affinity of the SARS-CoV-2 spike (S)-protein {Delta}{Delta}Gbind to the human membrane protein ACE2 is critical for virus function and evolution. Computational structure-based screening of new S-protein mutations for ACE2 binding lends promise to rationalize virus function directly from protein structure and ideally aid early detection of potentially concerning variants. We used a computational protocol based on cryo-electron microscopy structures of the S-protein to estimate the ACE2-binding that gave good trend agreement with experimental ACE2 affinities. We then expanded predictions to all possible S-protein mutations in 21 different S-protein-ACE2 complexes (400,000 {Delta}{Delta}Gbind data points in total), using mutation group comparisons to reduce systematic errors. We show that mutations that have arisen in major variants as a group maintain ACE2 affinity significantly more than random mutations in the total protein, at the interface, and at evolvable sites, with differences between variant mutations being small relative to these effects. Omicron mutations as a group had a modest change in binding affinity compared to mutations in other major variants. The single-mutation effects are consistent with ACE2 binding being optimized and maintained in omicron, despite increased importance of other selection pressures (antigenic drift). As epistasis, glycosylation and in vivo conditions will modulate these effects, computational predictive SARS-CoV-2 evolution remains far from achieved, but the feasibility of large-scale computation is substantially aided by using many structures and comparison of mutation groups rather than single mutation effects, which are very uncertain. Our results demonstrate substantial challenges but indicate ways to improve the quality of computer models for assessing SARS-CoV-2 mutation effects.

biophysics↗

bioRxiv2022

Machine Learning Electroencephalography Biomarkers Predictive of Epworth Sleepiness Scale

Excessive daytime sleepiness (EDS) causes difficulty in concentrating and continuous fatigue during the day. In a clinical setting, the assessment and diagnosis of EDS relies mostly on subjective questionnaires and verbal reports, which compromises the effectiveness of available therapies. In this study, we used a computational pipeline for the automated, rapid, high-throughput and objective analysis of retrospective encephalography (EEG) data to develop objective, surrogate biomarkers for EDS, thereby defining the quantitative EEG changes in individuals with High Epworth Sleepiness Scale (ESS) (n=31), compared to a group of individuals with Low ESS (n=41) at Cleveland Clinic. Signal processing of EEG showed significantly different EEG features in the Low ESS group compared to High ESS, including power enhancement in the alpha and beta bands, and attenuation in the delta and theta bands. Moreover, machine learning algorithms trained on the binary classification of High vs Low ESS reached >80% accuracy. These results demonstrate that EEG data contain information in the form of rhythmic activity that could be leveraged for the quantitative assessment of EDS using machine learning.

neuroscience↗

bioRxiv2022

Stability and expression of SARS-CoV-2 spike-protein mutations

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen for ACE2 binding. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

biophysics↗

bioRxiv2021

Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment

BackgroundDementia-like cognitive impairment is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of causative processes by which COVID-19 may lead to cognitive impairment is essential for developing preventive interventions. MethodsIn this study, we conducted a network-based, multimodal genomics comparison of COVID-19 and neurologic complications. We constructed the SARS-CoV-2 virus-host interactome from protein-protein interaction assay and CRISPR-Cas9 based genetic assay results, and compared network-based relationships therein with those of known neurological manifestations using network proximity measures. We also investigated the transcriptomic profiles (including single-cell/nuclei RNA-sequencing) of Alzheimers disease (AD) marker genes from patients infected with COVID-19, as well as the prevalence of SARS-CoV-2 entry factors in the brains of AD patients not infected with SARS-CoV-2. ResultsWe found significant network-based relationships between COVID-19 and neuroinflammation and brain microvascular injury pathways and processes which are implicated in AD. We also detected aberrant expression of AD biomarkers in the cerebrospinal fluid and blood of patients with COVID-19. While transcriptomic analyses showed relatively low expression of SARS-CoV-2 entry factors in human brain, neuroinflammatory changes were pronounced. In addition, single-nucleus transcriptomic analyses showed that expression of SARS-CoV-2 host factors (BSG and FURIN) and antiviral defense genes (LY6E, IFITM2, IFITM3, and IFNAR1) was significantly elevated in brain endothelial cells of AD patients and healthy controls relative to neurons and other cell types, suggesting a possible role for brain microvascular injury in COVID-19-mediated cognitive impairment. Notably, individuals with the AD risk allele APOE E4/E4 displayed reduced levels of antiviral defense genes compared to APOE E3/E3 individuals. ConclusionOur results suggest significant mechanistic overlap between AD and COVID-19, strongly centered on neuroinflammation and microvascular injury. These results help improve our understanding of COVID-19-associated neurological manifestations and provide guidance for future development of preventive or treatment interventions.

systems biology↗

bioRxiv2020

Multimodal Single-Cell Omics Analysis of COVID-19 Sex Differences in Human Immune Systems

Sex differences in the risk of SARS-CoV-2 infection have been controversial and the underlying mechanisms of COVID-19 sexual dimorphism remain understudied. Here we inspected sex differences in SARS-CoV-2 positivity, hospitalization, admission to the intensive care unit (ICU), sera immune profiling, and two single-cell RNA-sequencing (snRNA-seq) profiles from nasal tissues and peripheral blood mononuclear cells (PBMCs) of COVID-19 patients with varying degrees of disease severity. Our propensity score-matching observations revealed that male individuals have a 29% increased likelihood of SARS-CoV-2 positivity, with a hazard ration (HR) 1.32 (95% confidence interval [CI] 1.18-1.48) for hospitalization and HR 1.51 (95% CI 1.24-1.84) for admission to ICU. Sera from male patients at hospital admission had decreased lymphocyte count and elevated inflammatory markers (C-reactive protein, procalcitonin, and neutrophils). We found that SARS-CoV-2 entry factors, including ACE2, TMPRSS2, FURIN and NRP1, have elevated expression in nasal squamous cells from males with moderate and severe COVID-19. Cell-cell network proximity analysis suggests possible epithelium-immune cell interactions and immune vulnerability underlying a higher mortality in males with COVID-19. Monocyte-elevated expression of Toll like receptor 7 (TLR7) and Bruton tyrosine kinase (BTK) is associated with severe outcomes in males with COVID-19. These findings provide basis for understanding immune responses underlying sex differences, and designing sex-specific targeted treatments and patient care for COVID-19.

systems biology↗

bioRxiv2020

Understanding photothermal interactions will help expand production range and increase genetic diversity of lentil (Lens culinaris Medik.)

O_LILentil (Lens culinaris Medik.) is cultivated under a wide range of environmental conditions, which led to diverse phenological adaptations and resulted in a decrease in genetic variability within breeding programs due to reluctance in using genotypes from other environments. C_LIO_LIWe phenotyped 324 genotypes across nine locations over three years to assess their phenological response to the environment of major lentil production regions and to predict days from sowing to flowering (DTF) using a photothermal model. C_LIO_LIDTF was highly influenced by the environment and is sufficient to explain adaptation. We were able to predict DTF reliably in most environments using a simple photothermal model, however, in certain site-years, results suggest there may be additional environmental factors at play. Hierarchical clustering of principal components revealed the presence of eight groups based on the responses of DTF to contrasting environments. These groups are associated with the coefficients of the photothermal model and revealed differences in temperature and photoperiod sensitivity. C_LIO_LIExpanding genetic diversity is critical to the success of a breeding program; understanding adaptation will facilitate the use of exotic germplasm. Future climate change scenarios will result in increase temperature and/or shifts in production areas, we can use the photothermal model to identify genotypes most likely to succeed in these new environments. C_LI

plant biology↗

bioRxiv2018

Notch3 Promotes Prostate Cancer-Induced Bone Osteoblastic Metastasis in an MMP-3-Dependent Manner

Prostate cancer metastases primarily localize in the bone where they induce a unique osteoblastic response. Elevated Notch activity is associated with high-grade disease and metastasis. To address how Notch affects prostate cancer bone lesions, we manipulated Notch expression in mouse tibia xenografts and monitored tumor growth, lesion phenotype, and the bone microenvironment. Prostate cancer cell lines that induce mixed osteoblastic lesions in bone expressed 5-6 times more Notch3, than tumor cells that produce osteolytic lesions. Expression of active Notch3 (NICD3) in osteolytic tumors reduced osteolytic lesion area and enhanced osteoblastogenesis, while loss of Notch3 in osteoblastic tumors enhanced osteolytic lesion area and decreased osteoblastogensis. This was accompanied by a respective decrease and increase in the number of active osteoclasts and osteoblasts at the tumor-bone interface, without any effect on tumor proliferation. Conditioned medium from NICD3-expressing cells enhanced osteoblast differentiation and proliferation in vitro, while simultaneously inhibiting osteoclastogenesis. MMP-3 was specifically elevated and secreted by NICD3-expressing tumors, and inhibition of MMP-3 rescued the NICD3-induced osteoblastic phenotypes. Clinical osteoblastic bone metastasis samples had higher levels of Notch3 and MMP-3 compared to patient matched visceral metastases or osteolytic metastasis samples. We identified a Notch3-MMP-3 axis in human prostate cancer bone metastases that contributes to osteoblastic lesion formation by blocking osteoclast differentiation, while also contributing to osteoblastogenesis. These studies define a new role for Notch3 in manipulating the tumor microenvironment in bone metastases.

cancer biology↗