Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning

Histology imaging is an important tool in medical diagnosis and research, enabling the examination of tissue structure and composition at the microscopic level. Understanding the underlying molecular mechanisms of tissue architecture is critical in uncovering disease mechanisms and developing effective treatments. Gene expression profiling provides insight into the molecular processes underlying tissue architecture, but the process can be time-consuming and expensive. In this study, we present BLEEP (Bi-modaL Embedding for Expression Prediction), a bi-modal embedding framework capable of generating spatially resolved gene expression profiles of whole-slide Hematoxylin and eosin (H&E) stained histology images. BLEEP uses a contrastive learning framework to construct a low-dimensional joint embedding space from a reference dataset using paired image and expression profiles at micrometer resolution. With this framework, the gene expression of any query image patch can be imputed using the expression profiles from the reference dataset. We demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking its performance on a human liver tissue dataset captured via the 10x Visium platform, where it achieves significant improvements over existing methods. Our results demonstrate the potential of BLEEP to provide insights into the molecular mechanisms underlying tissue architecture, with important implications in diagnosis and research of various diseases. The proposed framework can significantly reduce the time and cost associated with gene expression profiling, opening up new avenues for high-throughput analysis of histology images for both research and clinical applications

Transformation of \u3ci\u3eFusarium verticillioides\u3c/i\u3e with a polyketide gene cluster isolated from a fungal endophyte activates the biosynthesis of fusaric acid

A large number of bioactive natural products have been isolated from plant endophytic fungi. However, molecular mechanisms for the biosynthesis of these metabolites have lagged behind because genetic and biochemical studies are difficult to perform within many of the endophytes. In this work, we describe our attempt to express a putative mycoepoxydiene (MED) biosynthetic gene cluster in Fusarium verticillioides, which has a well-developed genetic system for the study fungal polyketide biosynthesis. MED was isolated from Phomopsis sp. A123, a fungal endophyte of the mangrove plant, Kandelia candel. It has several unusual structural features and interesting biological activities. Integration of this Phomopsis gene cluster into the F. verticillioides genome led to the biosynthesis of multiple metabolites. The most highly activated metabolite was isolated and its structure was shown by 1D- and 2D-NMR to be fusaric acid, which is a mycotoxin in Fusarium species and is implicated in fungal pathogenesis. Although fusaric acid was isolated more than 70 years ago, its biosynthetic mechanism remains unclear. These transformants produced 30–35 mg fusaric acid per 100 ml culture. The high level production of fusaric acid will greatly facilitate the genetic and biochemical study of its biosynthetic mechanism. Although we have not detected MED or its analogs from the heterologous host, this work represents the first attempt to express a fungal endophytic gene cluster in a Fusarium species

Iterative Assembly of Two Separate Polyketide Chains by the Same Single-module Bacterial Polyketide Synthase in the Biosynthesis of HSAF

HSAF (1) was isolated from the biocontrol agent Lysobacter enzymogenes (Figure 1).[1-4] This bacterial metabolite belongs to polycyclic tetramate macrolactams (PTM) that are emerging as a new class of natural products with distinct structural features. [5, 6] HSAF exhibits a potent antifungal activity and shows a novel mode of action.[1-4] The HSAF biosynthetic gene cluster contains only a single-module hybrid polyketide synthasenonribosomal peptide synthetase (PKS-NRPS), although the PTM scaffold is apparently derived from two separate hexaketide chains and an ornithine residue.[1-4] This suggests that the same PKS module would act not only iteratively, but also separately, in order to link the two hexaketide chains with the NRPS-activated ornithine to form the characteristic PTM scaffold. Recently, the Gulder group reported heterologous expression of the ikarugamycin (4) biosynthetic gene cluster in E. coli,[7] and the Zhang group reported the enzymatic mechanism for formation of the inner 5-memebered ring and demonstrated the polyketide origin of the ikarugamycin skeleton.[8] Ikarugamycin is a Streptomyces-derived PTM which has a 5,6,5-tricyclic system (Figure 1). Both the Gulder and Zhang groups showed that a three-gene cluster is sufficient for ikarugamycin biosynthesis. Despite the progress, this iterative polyketide biosynthetic mechanism had not been demonstrated using purified PKS and NRPS. In addition, HSAF has a 5,5,6-tricyclic system, and its gene cluster contains at least six genes.[3] Finally, unlike most PTM compounds, HSAF is produced by a Gramnegative bacterium, L. enzymogenes. Here, we report the heterologous production of HSAF analogs in Gram-positive Streptomyces hosts, in which the native PKS have been deleted. We also obtained evidence for the formation of the polyene tetramate intermediate in Streptomyces when only the single-module hybrid PKS-NRPS gene was expressed. Finally, we showed the in vitro production of the polyene tetramate using the individually purified PKS and NRPS. The results provide direct evidence for this iterative polyketide biosynthetic mechanism that is likely general for the PTM-type hybrid polyketide-peptides

IMECE2003-44486 NUMERICAL DETERMINATION OF THE MOMENT LYAPUNOV EXPONENTS

Abstract Two numerical methods for the determination of the pth moment Lyapunov exponents of a two-dimensional system under bounded noise or real noise parametric excitation are presented. The first method is an analytical-numerical approach, in which the partial differential eigenvalue problems governing the moment Lyapunov exponents are established using the theory of stochastic dynamical systems. The eigenfunctions are expanded in double series to transform the partial differential eigenvalue problems to linear algebraic eigenvalue problems, which are then solved numerically. The second method is a Monte Carlo simulation approach. The numerical values obtained are compared with approximate analytical results with weak noise amplitudes

WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models

This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.Comment: Camera-ready submission to ClinicalNLP @ ACL 202

Mutated in colorectal cancer (MCC) is a novel oncogene in B lymphocytes

BACKGROUND: Identification of novel genetic risk factors is imperative for a better understanding of B lymphomagenesis and for the development of novel therapeutic strategies. TRAF3, a critical regulator of B cell survival, was recently recognized as a tumor suppressor gene in B lymphocytes. The present study aimed to identify novel oncogenes involved in malignant transformation of TRAF3-deficient B cells. METHODS: We used microarray analysis to identify genes differentially expressed in TRAF3(−/−) mouse splenic B lymphomas. We employed lentiviral vector-mediated knockdown or overexpression to manipulate gene expression in human multiple myeloma (MM) cell lines. We analyzed cell apoptosis and proliferation using flow cytometry, and performed biochemical studies to investigate signaling mechanisms. To delineate protein-protein interactions, we applied affinity purification followed by mass spectrometry-based sequencing. RESULTS: We identified mutated in colorectal cancer (MCC) as a gene strikingly up-regulated in TRAF3-deficient mouse B lymphomas and human MM cell lines. Aberrant up-regulation of MCC also occurs in a variety of primary human B cell malignancies, including non-Hodgkin lymphoma (NHL) and MM. In contrast, MCC expression was not detected in normal or premalignant TRAF3(−/−) B cells even after treatment with B cell stimuli, suggesting that aberrant up-regulation of MCC is specifically associated with malignant transformation of B cells. In elucidating the functional roles of MCC in malignant B cells, we found that lentiviral shRNA vector-mediated knockdown of MCC induced apoptosis and inhibited proliferation in human MM cells. Experiments of knockdown and overexpression of MCC allowed us to identify several downstream targets of MCC in human MM cells, including phospho-ERK, c-Myc, p27, cyclin B1, Mcl-1, caspases 8 and 3. Furthermore, we identified 365 proteins (including 326 novel MCC-interactors) in the MCC interactome, among which PARP1 and PHB2 were two hubs of MCC signaling pathways in human MM cells. CONCLUSIONS: Our results indicate that in sharp contrast to its tumor suppressive role in colorectal cancer, MCC functions as an oncogene in B cells. Our findings suggest that MCC may serve as a diagnostic marker and therapeutic target in B cell malignancies, including NHL and MM. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13045-014-0056-6) contains supplementary material, which is available to authorized users

Chromosome-Level Genome Assembly for Acer pseudosieboldianum and Highlights to Mechanisms for Leaf Color and Shape Change

Acer pseudosieboldianum (Pax) Komarov is an ornamental plant with prominent potential and is naturally distributed in Northeast China. Here, we obtained a chromosome-scale genome assembly of A. pseudosieboldianum combining HiFi and Hi-C data, and the final assembled genome size was 690.24 Mb and consisted of 287 contigs, with a contig N50 value of 5.7 Mb and a BUSCO complete gene percentage of 98.4%. Genome evolution analysis showed that an ancient duplication occurred in A. pseudosieboldianum. Phylogenetic analyses revealed that Aceraceae family could be incorporated into Sapindaceae, consistent with the present Angiosperm Phylogeny Group system. We further construct a gene-to-metabolite correlation network and identified key genes and metabolites that might be involved in anthocyanin biosynthesis pathways during leaf color change. Additionally, we identified crucial teosinte branched1, cycloidea, and proliferating cell factors (TCP) transcription factors that might be involved in leaf morphology regulation of A. pseudosieboldianum, Acer yangbiense and Acer truncatum. Overall, this reference genome is a valuable resource for evolutionary history studies of A. pseudosieboldianum and lays a fundamental foundation for its molecular breeding

Global analyses of human immune variation reveal baseline predictors of postvaccination responses.

A major goal of systems biology is the development of models that accurately predict responses to perturbation. Constructing such models requires the collection of dense measurements of system states, yet transformation of data into predictive constructs remains a challenge. To begin to model human immunity, we analyzed immune parameters in depth both at baseline and in response to influenza vaccination. Peripheral blood mononuclear cell transcriptomes, serum titers, cell subpopulation frequencies, and B cell responses were assessed in 63 individuals before and after vaccination and were used to develop a systematic framework to dissect inter- and intra-individual variation and build predictive models of postvaccination antibody responses. Strikingly, independent of age and pre-existing antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time points. Most of the parameters contributing to prediction delineated temporally stable baseline differences across individuals, raising the prospect of immune monitoring before intervention