Biology subjects

Cheung, M.

Publications and source records attributed to Cheung, M..

17 recordsLinked to original sources

bioRxiv2026

Cell State Transitions Drive Transcriptome-Proteome Alignment in a Minimal Photoautotroph

The cyanobacterium Prochlorococcus marinus MED4 is a minimal photoautotroph that maintains circadian regulation despite its reduced circadian regulatory toolkit relative to other cyanobacteria. Moreover, MED4, like other small prokaryotes, has low transcript counts per cell and poor transcriptome-proteome alignment. A systems-level understanding of how MED4 uses its minimal regulatory machinery to achieve a well-coordinated circadian cycle is currently lacking. We address this knowledge gap by analyzing global proteomics, transcriptomics, metabolomics, and lipidomics collected across a circadian day. Integrating these data with statistical analyses, enzyme-constrained metabolic modeling, and stochastic mathematical modeling allows us to reveal and characterize six major population-level cell states. Here we present evidence that high-light response regulator RpaB plays an important night-time regulatory role in MED4 by inhibiting RpaA, the master transcriptional activator of dusk genes and HimA, a regulatory target of RpaA previously shown to influence DNA conformation, is highly correlated with global transcriptome-proteome alignment (R2 = 0.67, p < 0.001). We construct stochastic differential equations consistent with genome-wide promotion of transcription by HimA as a plausible mechanism. Our HimA-driven model recapitulates alignment dynamics (R2 = 0.59, p < 0.01) as one plausible explanation. Collectively, our results elucidate how a minimal circadian regulatory program achieves robust coordination across transcriptional, translational, and metabolic levels

systems biology↗

bioRxiv2026

Novel Methylation Markers in a Prostate Cancer Cohort are Associated with Disease Development and Relapse

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

cancer biology↗

bioRxiv2026

Five Rotational Registers Define Modular Portal-Capsid Assembly in Cyanophage P-SSP7

The virus P-SSP7 infects the cyanobacterium Prochlorococcus marinus, one of the most abundant photosynthetic microbes in the ocean, making this pairing a useful model for studying host-virus interactions. Infection proceeds through a portal-tail complex that attaches to the viral protein shell at a single specialized vertex, where the two structures have mismatched symmetries. Previous studies described this region only at coarse resolution which continues to limit understanding of how the virus assembles and injects its genome. Using cryo-electron microscopy of high-quality samples, we determined the first near-atomic-resolution structure of the complete P-SSP7 virion and its portal-tail complex. We captured 3D classes where the shell and portal-tail complex connect in five distinct arrangements. Fitting and modeling 2 of these states helped resolve and establish modular attachment in five rotational registers as the basis for capsid-portal symmetry mismatch in P-SSP7 assembly.

microbiology↗

bioRxiv2026

Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition

Determining the structural basis of antigen recognition by antibodies and T cell receptors (TCRs) provides critical insights into effective immune targeting and can inform design of biotherapeutics and vaccines. Accurate computational modeling of antibodies and TCRs in complex with their targets poses a major challenge for predictive methods, including AlphaFold, which is generally accurate for modeling protein complexes but has shown limited success for immune recognition. In this study we assessed the performance of AlphaFold2, AlphaFold3, increased sampling protocols, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-peptide-major histocompatibility complex (pMHC) recognition. We show that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size. Comparing per-case success across methods showed some complementarity, indicating opportunities for increased success through model pooling approaches, for instance increasing antibody-peptide near-native success from 41% to 59%. Analysis of AlphaFold confidence scores and modeling of a noncanonical complex provided further insights into predictive performance. These results highlight considerations for predictive antibody and TCR complex modeling efforts, while revealing key distinctions among protocols, scoring, and immune complex classes.

bioinformatics↗

bioRxiv2026

Stress-Related Transcriptional Regulators Dominate the Conserved Core GRN for Three Cyanobacteria: Network Topology Maps the Highest-Influence Nodes as Promising Engineering Targets

Cyanobacteria manage photosynthetic and environmental stresses through transcriptional programs controlled by regulators also affecting carbon flux, growth states, and metabolic output that bioproduction seeks to optimize. This regulatory architecture and its most influential nodes remain incompletely characterized. We hypothesized two influential regulator layers: a conserved core responding to common stresses, and species-specific regulators mediating strain-level niche adaptations. Mapping both layers underpins understanding genome[->]regulatory-network[->]phenotype flow, enabling global transcription machinery engineering for reliable bioproduction. To test our hypothesis, we constructed conserved-core and species-specific gene regulatory networks (GRNs) for three cyanobacteria, Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803 and Picosynechococcus sp. PCC 7002, integrating a manually curated multi-pipeline regulator inventory with 1,098 harmonized transcriptome states for the 1,362-gene tri-homolog core genome. We quantified each regulator influence using local (degree, k-core), global (betweenness, closeness), and community-aware (eigenvector) centrality measures, and an Integrated Centrality score aggregating influence across complementary topological measures. High-influence regulators are predicted to exert broad metabolic effects when manipulated, making them priority candidates for single-target engineering interventions that modulate multiple genes and reprogram complex phenotypes. Across the three cyanobacteria, the two GRN layers proved topologically distinct: the conserved core concentrated influence in stress-related hubs (11 of its top 15 by Integrated Centrality were stress-related), while species-specific networks spread influence across functionally diverse regulators. Stress-coupled enrichment also held per individual centrality measure: regulators ranking top in both the core and species-specific GRNs by the same measure were mostly stress-related (15 of 19 instances), including the multi-stress regulators RpaB, Rre1, and BolA, the heat-shock HrcA, and the nitrogen NtcA. In species-specific GRNs, stress-related regulators remained the leading category alongside circadian, carbon-metabolism, morphology, and housekeeping regulators, including PlmA, Pex, TetR, and SrrB in PCC 7942; KaiC3, Sycrp1, Rre28, and Bhl in PCC 6803; and Zur, Sycrp1, and NarL in PCC 7002. High-influence putative regulators included the iron-stress AraC-family paralogs IutR1-IutR3, OmpR-family paralogs OmpR1-OmpR2, and chromosome- or plasmid-encoded Xre-family, AraC, and HypP. Stress regulation emerges as a recurring high-influence axis across these networks. The conserved core identifies universal regulatory programs, and species-specific layers reveal strain-level innovations for cross-strain transfer to support engineering of robust bioproduction. ImportanceCyanobacteria are studied as platforms for sustainable, carbon-recycling production of fuels and chemicals from sunlight, water, and atmospheric carbon dioxide. Their reliable deployment in industrial settings is limited by environmental stresses that depress photosynthetic efficiency and product yields. The same regulatory proteins that govern stress responses also control how cells partition carbon, switch growth states, and direct metabolic output, making them natural levers for engineering robust production strains. Yet systematic, cross-species maps of these regulators have been missing. We present the first comparative regulatory map spanning three biotechnologically important model cyanobacteria, Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803, and Picosynechococcus sp. PCC 7002, and identify the conserved regulators most influential across all three. The resulting catalog prioritizes candidate targets for experimental validation, and the supporting datasets and analytical framework are released for reuse to support efforts to engineer cyanobacterial strains for reliable industrial bioproduction.

systems biology↗

bioRxiv2026

Extreme genome reduction selectively retains modular regulatory architecture in Prochlorococcus MED4: conserved transcriptional modules reveal core physiological regulatory programs

Prochlorococcus MED4 is a minimal photoautotroph whose extreme genome streamlining extends to its transcriptional regulatory architecture, yet it dominates high-light oligotrophic surface waters and drives marine carbon cycling. Despite ecological significance, MED4 remains genetically intractable, lacking molecular tools to characterize regulatory mechanisms and construct a transcriptome-wide regulatory map. To address this, we assembled an RNA-seq compendium of 253 samples, including 207 new samples capturing transcriptional responses across three classes of experiments: diverse environmental perturbations, a 24-hour circadian cycle, and phage infection. Using independent component analysis (ICA) applied to 247 quality-filtered samples, we identify 32 independently regulated gene set modules in MED4 (iModulons). By comparison, we previously identified 78 iModulons in the model cyanobacterium Synechococcus elongatus PCC 7942, revealing how dramatically genome reduction has simplified MED4s regulatory architecture. Of the 32 iModulons in MED4, 13 are conserved modules that correlate with experimentally validated transcriptional regulons in PCC 7942, identifying regulatory programs that resisted elimination under extreme selective pressure. These conserved modules reveal regulatory programs governing photosynthesis and light responses (RpaB), circadian rhythms (RpaA), and nutrient assimilation (NtcA, PhoB). Known regulator-specific DNA-binding motifs upstream of genes in conserved modules independently support their identification as regulatory targets. Notably, RpaA governs circadian rhythms through three temporally distinct modules in MED4 versus one in PCC 7942, and the RpaB photoprotection module similarly splits into two. This work uncovers the minimal regulatory core governing photosynthesis, circadian rhythms, and C/N/P metabolism in a globally critical but genetically intractable photoautotroph. This approach offers a generalizable framework for regulatory inference beyond model organisms. ImportanceThe minimal photoautotroph Prochlorococcus MED4 possesses only 28 transcriptional regulators, versus 150+ found in most cyanobacteria, reflecting a genome streamlined by billions of years of natural selection. This streamlining minimized metabolic costs, enabling MED4 to dominate nutrient-depleted oceans today. This raises a fundamental question: which regulatory programs did nature choose to keep? The answer matters, but MED4 cannot be studied by conventional genetics, placing its molecular machinery beyond direct experimental reach. Instead, we use large-scale computational methods to define groups of co-regulated genes in MED4. By comparing MED4 with a genetically tractable model cyanobacterium, we can distinguish which regulatory programs nature preserved from those it discarded. This work reveals the minimal regulatory architecture sufficient to sustain the smallest oxygenic photoautotroph on Earth. The principles uncovered here, distinguishing essential from dispensable regulatory programs in a naturally streamlined organism, inform the design of minimal photosynthetic platforms for biotechnology.

systems biology↗

bioRxiv2026

Systematic Scale-Up and Enhanced Purification of Marine Cyanophage P-SSP7

Cyanophages represent important models for understanding virus-host interactions, yet high-resolution structural studies remain relatively few due to challenges with preparing enough sample of sufficient quality for cryo-EM and functional multi-omics studies. Here we developed an integrated methodology for scaling production of the model cyanophage P-SSP7 from laboratory maintenance volumes (5-100 mL) to production scales (up to 40 L) while dramatically improving the quality of phage preparation for structural applications. Our systematic approach integrates host cultivation using adaptation to local seawater to reduce production costs, optimized infection protocols to maximize infectious titer yields, and multi-stage purification workflows specifically designed for cryo-EM quality requirements. The final methodology consistently produces infectious phage titers exceeding 3x1012 units/mL with recoverable yields of 1013 total infectious units and >95% purity validated by cryo-electron microscopy at each optimization step. Most significantly, this approach achieves a 60-fold reduction in cryo-EM data collection time by increasing usable particles per field of view for single particle analysis. Overall, our final preparations demonstrate robust phage stability, retaining 68% infectivity after 3 months and 23% after 6 months at 4{degrees}C. This workflow moves cyanophage culturing and downstream structural studies from specialized, resource-intensive endeavors toward routine research capability and establishes an adaptable framework for scaling production that can be applied to other host-virus systems.

microbiology↗

bioRxiv2025

Modular cyanobacterial regulatory architecture enables dynamic graded responses to oxidative stress

A fundamental paradox of oxygenic photosynthesis: growth-essential energy machinery generates reactive oxygen species (ROS) threatening survival, yet the systems-level regulatory networks balancing the growth-survival trade-off remain unclear. Through integrative experimental-computational analysis combining steady-state transcriptomics with independent component analysis across 0-78.4% oxygen, we decoded the regulatory architecture driving progressive transitions in Synechococcus elongatus PCC 7942 from ROS sensing through defense to growth shutdown. Integration of 407 transcriptome samples identified 78 regulatory modules (iModulons) explaining 72.3% of expression variance and revealed calibrated responses: low stress triggers metalloregulators (SufR, PerR) for ROS sensing and primary antioxidants; moderate stress activates RpaB operating through four distinct regulatory states redirecting metabolism to defense; severe stress induces growth arrest via stringent response pathway convergence. This quantitative regulatory framework enables precise growth-defense calibration through modular network architecture: RpaB coordinates genome-wide resource reallocation (RpaB[~]P growth-promoting, RpaB ROS defense-activating, RpaABC circadian-integrating, RpaB ycf46 checkpoint activation), offering systematic strategies for engineering stress-tolerant bioplatforms and predictive models for environmental stress responses.

systems biology↗

bioRxiv2024

Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens

T cell receptors (TCRs) that recognize cancer neoantigens are important for anti-cancer immune responses and immunotherapy. Understanding the structural basis of TCR recognition of neoantigens provides insights into their exquisite specificity and can enable design of optimized TCRs. We determined crystal structures of a human TCR in complex with NRAS Q61K and Q61R neoantigen peptides and HLA-A1 MHC, revealing the molecular underpinnings for dual recognition and specificity versus wild-type NRAS peptide. We then used multiple versions of AlphaFold to model the corresponding complex structures, given the challenge of immune recognition for such methods. Interestingly, one implementation of AlphaFold2 (TCRmodel2) was able to generate accurate models of the complexes, while AlphaFold3 also showed strong performance, although success was lower for other complexes. This study provides insights into TCR recognition of a shared cancer neoantigen, as well as the utility and practical considerations for using AlphaFold to model TCR-peptide-MHC complexes.

immunology↗

bioRxiv2024

Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively unexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

bioinformatics↗

bioRxiv2023

Emergence of a senescent and inflammatory pulmonary CD4+ T cell population prior to lung allograft failure

Lung transplantation is a life-saving therapy for end-stage pulmonary disease, but its long-term outlook is poor due to a high incidence of chronic lung allograft dysfunction (CLAD). CLAD results from alloimmune-mediated progressive fibrosis and culminates in death or the need for re-transplantation after a median of 6 years. Existing immunosuppression fails to prevent CLAD, suggesting the existence of alloimmune pathways resistant to these drugs. Here, we used mass cytometry to identify cell populations enriched in the bronchoalveolar lavage (BAL) of patients with subsequent allograft dysfunction. We show that CD4+CD57+PD1+ T cells emerge in stable lung transplant recipients in the first year post-transplant, conferring heightened risks for CLAD and death or re-transplantation. CD4+CD57+PD1+ T cells display features of senescence and secrete inflammatory cytokines. Cellular indexing by transcriptomes and epitopes (CITE-Seq) on BAL CD4+ T cells revealed the existence of 2 oligoclonal CD57+ subsets with putative cytotoxic and follicular helper functions. Finally, we observed that CD4+CD57+PD1+ T cells are associated with lung allograft fibrosis in a mouse model and in human explanted CLAD lungs, where they localize near airway epithelium and B cells. Together, our findings reveal the existence of an inflammatory T cell population that predicts future lung allograft dysfunction and may represent a rational therapeutic target in lung transplant recipients.

immunology↗

bioRxiv2021

Shared and distinct genetic features in human and canine B-cell lymphomas

Animal models of human cancers are an important tool for the development and preclinical evaluation of therapeutics. Canine B-cell lymphoma (cBCL) is an appealing model for human mature B-cell neoplasms due to the high sequence similarity in cancer genes to humans and inactive telomerase in adult tissues. We performed targeted sequencing on 86 canine patients from the Canine Comparative Oncology Genomic Consortium, with 61 confirmed as B-cell lymphomas. We confirmed a high frequency of mutations in TRAF3 (45%) and FBXW7 (20%) as has been reported by our group and others. We also note a higher frequency of DDX3X (20%) and MYC (13%) mutations in our canine cohort. We compared the pattern and incidence of mutations in cBCL to human diffuse large B-cell lymphoma (hDLBCL) and human Burkitt lymphoma (hBL). Canine MYC mutations displayed a focal pattern with 80% of mutations affecting the conserved phosphodegron sequence in MYC box 1, which are known to stabilize MYC protein. We also note that MYC and FBXW7 mutations do not co-occur in our cBCL cohort, leading to the hypothesis that these mutations represent alternative approaches to stabilize MYC in canine lymphoma. We observed striking differences in the pattern of DDX3X mutations in canine lymphoma as compared to hBL and uncovered a sex-specific pattern of DDX3X mutations in hBL that is not consistent with those identified in canine lymphomas. In sum, we describe key differences between cBCL and human mature B-cell lymphomas which may indicate differences in the biology of these cancers. This should be considered in future studies of cBCL as a model of human lymphomas.

cancer biology↗

bioRxiv2020

Inactivation of p21-Activated Kinase 2 (Pak2) Inhibits the Development of Nf2-Deficient Malignant Mesothelioma

Malignant mesotheliomas (MM) show frequent somatic loss of the NF2 tumor suppressor gene. The NF2 product, Merlin, is implicated in several tumor-related pathways, including p21-activated kinase (PAK) signaling. Merlin is both a phosphorylation target for PAK and a negative regulator of this oncogenic kinase. Merlin loss results in PAK activation, and PAK inhibitors hold promise for the treatment of NF2-deficient tumors. To test this possibility in an in vivo genetic system, Nf2f/f;Cdkn2af/f mice were crossed to mice with conditional knockout of Pak2, a highly expressed group I Pak member. Cohorts of these animals were injected in either the thoracic or peritoneal cavities with adeno-Cre virus to delete floxed alleles in the mesothelial lining. Loss of Pak2 resulted in a markedly decreased incidence and delayed onset and progression of pleural and peritoneal MMs in Nf2;Cdkn2a-deficient (NC) mice, as documented by Kaplan-Meier survival curves and in vivo bioluminescent imaging. RNA-seq revealed that MMs from NC;Pak2-/- mice showed downregulated expression of genes involved in several oncogenic pathways (Wnt, Akt) when compared to MMs from mice retaining Pak2. Kinome profiling showed that, as compared to NC MM cells, NC;Pak2-/- MM cells had multiple kinase changes indicative of an epithelial to mesenchymal transition. Collectively, these findings suggest that NC;Pak2-/- MMs adapt by reprogramming their kinome and gene signature profiles to bypass the need for PAK activity via the activation of other compensatory oncogenic kinase pathways. The identification of such secondary pathways offers opportunities for rational combination therapies to circumvent resistance to anti-PAK drugs.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2020

'Phase Zero' clinical study platform combining broadband Vis/near-infrared spectroscopy and electrophysiology to study human brain organoid models of neurodevelopmental disorders

Homeostatic control of neuronal excitability by modulation of synaptic inhibition (I) and excitation (E) of the principal neurons is important during brain maturation. The fundamental features of in-utero brain developmental, including local synaptic E-I ratio and bioenergetics, can be modeled by cerebral organoids (CO) that have exhibited highly regular nested oscillatory network events. Therefore, we evaluated a Phase Zero clinical study platform combining broadband Vis/near-infrared(NIR) spectroscopy and electrophysiology to study E-I ratio based on the spectral exponent of local field potentials and bioenergetics based on the activity of mitochondrial Cytochrome-C Oxidase (CCO). We found a significant effect of the age of the healthy controls iPSC CO from 23 days to 3 months on the CCO activity ({chi}2(2,N=10)=20,p=4.5400e-05), and spectral exponent between 30-50Hz ({chi}2(2,N=16)=13.88,p=0.001). Also, a significant effect of drugs, choline (CHO), idebenone (IDB), R-alpha-lipoic acid plus acetyl-L-carnitine (LCLA), was found on the CCO activity ({chi}2(3,N=10)=25.44,p = 1.2492e-05), spectral exponent between 1-20Hz ({chi}2(3,N=16)=43.5,p=1.9273e-09) and 30-50Hz ({chi}2(3,N=16)=23.47, p=3.2148e-05) in 34 days old CO from schizophrenia (SCZ) patients iPSC. We present a multidimensional approach combining electrophysiology and Vis-NIR spectroscopy to complement traditional drug design approaches that can advance the system towards a normative parameter space.

bioengineering↗

bioRxiv2019

Pathway Commons: 2019 Update

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and [~]2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

bioinformatics↗

bioRxiv2019

The Creative Mind: Blending Oxytocinergic, Dopaminergic and Personality

In a fast-changing world, creative thinking (CT) is an extraordinary currency. Oxytocin (OT) is associated with CT and release of OT depends on ADP ribosyl-cyclases (CD38 and CD157). Moreover, CT as well as OTs mechanism of action are mediated via central dopaminergic pathways. Consequently, we evaluate the roles of CD38, CD157, dopamine receptor D2 (DRD2) and catechol-O-methyltransferase (COMT) peripheral gene expression in CT. Two principal domains of CT, divergent thinking and insight solving problems, were assessed using validated behavioral assessments. To facilitate discriminant validity, two established correlates of CT, trait Openness and fluid intelligence as well as age and sex were included in the regression model. In women, significant main effects (p<0.01) were positively associated with the expression CD38, CD15, and their interaction CD38 x CD157 controlling for Openness, fluid intelligence and age. Subsequent analysis on the subscale-level revealed significant main effects for CD157 and CD38 x CD157 in men specifically for divergent thinking. In women, significant (p<0.01) results are also observed for dopaminergic expression (DRD2, COMT, DRD2 x COMT). The full model (oxytocinergic and dopaminergic gene expressions, Openness, and fluid intelligence) explains a sizable 39% of the variance in females. Significant main effects are observed for CD38, CD157, DRD2 and COMT as well as their interactions (CD38xCD157 and DRD2xCOMT). In conclusion, we show that oxytocinergic and dopaminergic gene expression contribute significantly to the complex CT phenotype suggesting the notion that the perspective gained from examining the peripheral transcriptome meaningfully adds to understanding the landscape of creative thinking.\n\nSignificance StatementCreative thinking (CT) is a powerful driving force galvanizing progress and civilization. Towards better understanding the neurobiology of CT, we implement a gene expression strategy that is considered to capture not only genomic elements but also environmental signatures. We employ laboratory measures of CT (alternative uses test and insight problem solving), controlling for fluid intelligence and the personality trait of Openness. We focus on oxytocinergic and dopaminergic genes that contribute to the molecular architecture of CT. Oxytocinergic and dopaminergic gene expression significantly explains a robust 39% of the variance in CT. Notably, this study demonstrates the potential of the peripheral transcriptome towards tracing gene pathways underlying some complex cognitive behaviors such as creative thinking.

neuroscience↗

bioRxiv2018

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning

IntroductionThe number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute as standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive and non-standardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. MethodsTrichrome-stained images (n=275) from renal biopsies of 171 chronic kidney disease patients treated at the Boston Medical Center from 2009-12 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by three experts into three categories: (a) No glomerulus, (b) Normal or partially sclerosed glomerulus and (c) Globally sclerosed glomerulus. This led to identification of 751 unique images representing non{-}glomerular regions, 611 images with either normal or partially sclerosed (NPS) glomeruli and 134 images with globally sclerosed (GS) glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test data images to segment the GS glomeruli. ResultsThe CNN model was able to accurately discriminate non-glomerular images from NPS and GS images (Performance on test data - Accuracy: 92.67{+/-}2.02% and Kappa: 0.8681{+/-}0.0392). The segmentation model that was based on the CNN multi-label classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). ConclusionThis work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.

pathology↗