Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Genome assembly of the Chinese maize elite inbred line RP125 and its EMS mutant collection provide new resources for maize genetics research and crop improvement.

Maize is an important crop worldwide, as well as a valuable model with vast genetic diversity. Accurate genome and annotation information for a wide range of inbred lines would provide valuable resources for crop improvement and pan-genome characterization. In this study, we generated a high-quality de novo genome assembly (contig N50 of 15.43 Mb) of the Chinese elite inbred line RP125 using Nanopore long-read sequencing and Hi-C scaffolding, which yield highly contiguous, chromosome-length scaffolds. Global comparison of the RP125 genome with those of B73, W22, and Mo17 revealed a large number of structural variations. To create new germplasm for maize research and crop improvement, we carried out an EMS mutagenesis screen on RP125. In total, we obtained 5818 independent M2 families, with 946 mutants showing heritable phenotypes. Taking advantage of the high-quality RP125 genome, we successfully cloned 10 mutants from the EMS library, including the novel kernel mutant qk1 (quekou: "missing a small part" in Chinese), which exhibited partial loss of endosperm and a starch accumulation defect. QK1 encodes a predicted metal tolerance protein, which is specifically required for Fe transport. Increased accumulation of Fe and reactive oxygen species as well as ferroptosis-like cell death were detected in qk1 endosperm. Our study provides the community with a high-quality genome sequence and a large collection of mutant germplasm.

A General Strategy Toward Highly Fluorogenic Bioprobes Emitting across the Visible Spectrum.

A general approach toward highly fluorogenic probes across the visible spectrum for various analytes offers significant potential for engineering a wide range of bioprobes with diverse sensing and imaging functions. Here we show a facile and general strategy that involves introducing a new fluorogenic mechanism in boron dipyrromethene (BODIPY) dyes, based on the principle of stimuli-triggered dramatic reduction in the electron-withdrawing capabilities of the meso-substituents of BODIPYs. The fluorogenic mechanism has been demonstrated to be applicable in various BODIPYs with emission maxima ranging from green to far red (509, 585, and 660 nm), and the synthetic strategy allows access to a panel of highly fluorogenic bioprobes for various biomolecules and enzymes (H2O2, H2S, and protease) via introducing specific triggering motifs. The potency of the general design strategy is exemplified by its application to develop a mitochondria-targeting far-red probe capable of imaging of endogenous H2O2 in living cells.

Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization.

As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded ∼0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of ∼81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs.

TLR2 promotes macrophage recruitment and Streptococcus pneumoniae clearance during mouse otitis media.

BACKGROUND: The natural course of otitis media (OM) in most children is acute and self-limiting; however, approximately 10-20% of children can experience persistent or recurrent OM. Determining the host factors that influence outcome of OM will help us design better therapies. This study focused on the role of Toll-like receptor 2 (TLR2) in a pneumococcal OM mouse model. METHODS: The middle ears (MEs) of wild-type (WT) and TLR2-/- mice were inoculated with Streptococcus pneumoniae (Spn) serotype 19F via transbullar injection. ME TLR2 expression in WT mice was determined by qRT-PCR and immunofluorescence. ME pathological manifestations, inflammatory response, and pneumococcal clearance between WT and TLR2-/- mice were compared after Spn inoculation. RESULTS: TLR2 expression in ME mucosa was markedly enhanced following infection with Spn in WT mice. In contrast to WT mice, TLR2-/- mice exhibited unaffected early ME inflammatory response. During late stage of ME infection, however, the absence of TLR2 can lead to reduced macrophage recruitment, impaired Spn clearance, and prolonged ME inflammation. CONCLUSION: Our results demonstrate that TLR2 signaling is critical for bacterial clearance and timely resolution of inflammation in OM induced by Spn.

Genome-wide analysis of transcription factors involved in maize embryonic callus formation.

In this study, a maize inbred line with a strong capacity to induce embryonic callus, 18-599R, was used to analyze the transcription factors expressed during embryonic callus formation. A total of 1180 transcription factors were found to be expressed during three key stages of callus induction. Of these, compared with control, 361, 346 and 328 transcription factors were significantly downregulated during stages I, II and III, respectively. In contrast, 355, 372 and 401 transcription factors (TFs) were upregulated during the respective stages. We constructed a transcription factor-mediated regulatory network and found that plant hormone signal transduction was the pathway most significantly enriched among TFs. This pathway includes 48 TFs regulating cell enlargement, cell differentiation, cell division and cell dedifferentiation via the response to plant hormones. Through real-time polymerase chain reaction (PCR) and degradome sequencing, we identified 23 transcription factors that are regulated by miRNA. Through further analysis, ZmMYB138, a member of the MYB transcription factor family localized in the nucleus, was verified to promote embryonic callus formation in the maize embryo through GA signal transduction.

Transcriptomic changes during maize roots development responsive to Cadmium (Cd) pollution using comparative RNAseq-based approach.

The heavy metal cadmium (Cd), acts as a widespread environmental contaminant, which has shown to adversely affect human health, food safety and ecosystem safety in recent years. However, research on how plant respond to various kinds of heavy metal stress is scarcely reported, especially for understanding of complex molecular regulatory mechanisms and elucidating the gene networks of plant respond to Cd stress. Here, transcriptomic changes during Mo17 and B73 seedlings development responsive to Cd pollution were investigated and comparative RNAseq-based approach in both genotypes were performed. 115 differential expression genes (DEGs) with significant alteration in expression were found co-modulated in both genotypes during the maize seedling development; of those, most of DGEs were found comprised of stress and defense responses proteins, transporters, as well as transcription factors, such as thaumatin-like protein, ZmOPR2 and ZmOPR5. More interestingly, genotype-specific transcriptional factors changes induced by Cd stress were found contributed to the regulatory mechanism of Cd sensitivity in both different genotypes. Moreover, 12 co-expression modules associated with specific biological processes or pathways (M1 to M12) were identified by consensus co-expression network. These results will expand our understanding of complex molecular mechanism of response and defense to Cd exposure in maize seedling roots.

Identification of miRNAs and their target genes in developing maize ears by combined small RNA and degradome sequencing.

BACKGROUND: In plants, microRNAs (miRNAs) are endogenous ~22 nt RNAs that play important regulatory roles in many aspects of plant biology, including metabolism, hormone response, epigenetic control of transposable elements, and stress response. Extensive studies of miRNAs have been performed in model plants such as rice and Arabidopsis thaliana. In maize, most miRNAs and their target genes were analyzed and identified by clearly different treatments, such as response to low nitrate, salt and drought stress. However, little is known about miRNAs involved in maize ear development. The objective of this study is to identify conserved and novel miRNAs and their target genes by combined small RNA and degradome sequencing at four inflorescence developmental stages. RESULTS: We used deep-sequencing, miRNA microarray assays and computational methods to identify, profile, and describe conserved and non-conserved miRNAs at four ear developmental stages, which resulted in identification of 22 conserved and 21-maize-specific miRNA families together with their corresponding miRNA*. Comparison of miRNA expression in these developmental stages revealed 18 differentially expressed miRNA families. Finally, a total of 141 genes (251 transcripts) targeted by 102 small RNAs including 98 miRNAs and 4 ta-siRNAs were identified by genomic-scale high-throughput sequencing of miRNA cleaved mRNAs. Moreover, the differentially expressed miRNAs-mediated pathways that regulate the development of ears were discussed. CONCLUSIONS: This study confirmed 22 conserved miRNA families and discovered 26 novel miRNAs in maize. Moreover, we identified 141 target genes of known and new miRNAs and ta-siRNAs. Of these, 72 genes (117 transcripts) targeted by 62 differentially expressed miRNAs may attribute to the development of maize ears. Identification and characterization of these important classes of regulatory genes in maize may improve our understanding of molecular mechanisms controlling ear development.

Immune pathogenesis of idiopathic granulomatous mastitis: from etiology toward therapeutic approaches.

Idiopathic granulomatous mastitis (IGM) is a noncancerous, chronic inflammatory disorder of breast with unknown causes, posing significant challenges to the quality of life due to its high refractoriness and local aggressiveness. The typical symptoms of this disease involve skin redness, a firm and tender breast mass and mastalgia; others may include swelling, fistula, abscess (often without fever), nipple retraction, and peau d'orange appearance. IGM often mimics breast abscesses or malignancies, particularly inflammatory breast cancer, and is characterized by absent standardized treatment options, inconsistent patient response and unknown mechanism. Definite diagnosis of this disease relies on core needle biopsy and histopathological examination. The prevailing etiological theory suggests that IGM is an autoimmune disease, as some patients respond well to steroid treatment. Additionally, the presence of concurrent erythema nodosum or other autoimmune conditions supports the autoimmune nature of the disease. Based on current knowledge, this review aims to elucidate the autoimmune-favored features of IGM and explore its potential etiologies. Furthermore, we discuss the immune-mediated pathogenesis of IGM using existing research and propose immunotherapeutic strategies for managing this condition.

Overview of Artificial Intelligence in Breast Cancer Medical Imaging.

The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

Single-Cell Metabolomics in Hematopoiesis and Hematological Malignancies.

Aberrant metabolism contributes to tumor initiation, progression, metastasis, and drug resistance. Metabolic dysregulation has emerged as a hallmark of several hematologic malignancies. Decoding the molecular mechanism underlying metabolic rewiring in hematological malignancies would provide promising avenues for novel therapeutic interventions. Single-cell metabolic analysis can directly offer a meaningful readout of the cellular phenotype, allowing us to comprehensively dissect cellular states and access biological information unobtainable from bulk analysis. In this review, we first highlight the unique metabolic properties of hematologic malignancies and underscore potential metabolic vulnerabilities. We then emphasize the emerging single-cell metabolomics techniques, aiming to provide a guide to interrogating metabolism at single-cell resolution. Furthermore, we summarize recent studies demonstrating the power of single-cell metabolomics to uncover the roles of metabolic rewiring in tumor biology, cellular heterogeneity, immunometabolism, and therapeutic resistance. Meanwhile, we describe a practical view of the potential applications of single-cell metabolomics in hematopoiesis and hematological malignancies. Finally, we present the challenges and perspectives of single-cell metabolomics development.

Combinatorial Strategies With PD-1/PD-L1 Immune Checkpoint Blockade for Breast Cancer Therapy: Mechanisms and Clinical Outcomes.

As an emerging antitumor strategy, immune checkpoint therapy is one of the most promising anticancer therapies due to its long response duration. Antibodies against the programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) axis have been extensively applied to various cancers and have demonstrated unprecedented efficacy. Nevertheless, a poor response to monotherapy with anti-PD-1/PD-L1 has been observed in metastatic breast cancer. Combination therapy with other standard treatments is expected to overcome this limitation of PD-1/PD-L1 blockade in the treatment of breast cancer. In the present review, we first illustrate the biological functions of PD-1/PD-L1 and their role in maintaining immune homeostasis as well as protecting against immune-mediated tissue damage in a variety of microenvironments. Several combination therapy strategies for the combination of PD-1/PD-L1 blockade with standard treatment modalities have been proposed to solve the limitations of anti-PD-1/PD-L1 treatment, including chemotherapy, radiotherapy, targeted therapy, antiangiogenic therapy, and other immunotherapies. The corresponding clinical trials provide valuable estimates of treatment effects. Notably, several combination options significantly improve the response and efficacy of PD-1/PD-L1 blockade. This review provides a PD-1/PD-L1 clinical trial landscape survey in breast cancer to guide the development of more effective and less toxic combination therapies.

Microbiota and their metabolites potentiate cancer immunotherapy: Therapeutic target or resource for small molecule drug discovery?

Increasing evidence has proved that microbiota is not only the target of small molecule drugs but also an underexplored resource for developing small molecule drugs. Meanwhile, microbiota as a critical modulator of the immune system impacts the efficacy and toxicity of cancer immunotherapy. Harnessing microbiota or developing microbiota-derived medications provide novel therapeutic strategies to overcome resistance to cancer immunotherapy and immune-related adverse events (irAEs). In this review, we elucidate how microbiota and their metabolites impact anti-tumor immunity and immunotherapy efficacy and highlight the potential of microbiota and their metabolites as a resource for small molecule drug discovery. We further overview the current landscape of clinical trials evaluating the potential effect of microbiota and their metabolites on immunotherapy outcomes, presenting future trends in the field of microbiota-based therapies. Microbiota-based therapies are promising therapeutic options to promote therapeutic efficacy and diminish the toxicity of immunotherapy.

The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer.

Immune checkpoint blockade (ICB) therapy has evoked a prominent shift in anticancer therapy. Durable clinical antitumor activity to ICB has been observed in patients with ovarian cancer (OC). However, only a subset of patients derive clinical benefit, and immune-related adverse events (irAEs) caused by ICB therapy can lead to permanent tissue damage and even fatal consequences. It is thus urgent to develop predictive biomarkers to optimize patient outcomes and minimize toxicity risk. Herein, we review current predictive and prognostic biomarkers for checkpoint immunotherapy in OC and highlight emerging biomarkers to guide treatment with ICB. The prevalent biomarkers, such as PD-L1 expression status, tumor-infiltrating lymphocytes, mutational burden, and immune gene signatures, are further discussed. We provide a state-of-the-art survey on prognostic and predictive biomarkers for checkpoint immunotherapy and offer valuable information for guiding precision immunotherapy.

Tissue-specific transcriptomic analysis uncovers potential roles of natural antisense transcripts in Arabidopsis heat stress response.

Natural antisense transcripts (NATs) are an important class of non-coding ribonucleic acids (RNAs) that have been shown to regulate gene expression. Using strand-specific RNA sequencing, 36,317 NAT pairs were identified, and 5,536 were specifically expressed under heat stress. We found distinct expression patterns between vegetative and reproductive tissues for both coding genes and genes encoding NATs. Genes for heat-responsive NATs are associated with relatively high levels of H3K4me3 and low levels of H3K27me2/3. On the other hand, small RNAs are significantly enriched in sequence overlapping regions of NAT pairs, and a large number of heat-responsive NATs pairs serve as potential precursors of nat-siRNAs. Collectively, our results suggest epigenetic modifications and small RNAs play important roles in the regulation of NAT expression, and highlight the potential significance of heat-inducible NATs.

Application of artificial intelligence to the public health education.

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

The Evolution of Acquired Resistance to BRAFV600E kinase inhibitor Is Sustained by IGF1-Driven Tumor Vascular Remodeling.

As a hallmark of cancer, angiogenesis plays a pivotal role in carcinogenesis. However, the correlation between angiogenesis and the evolution of BRAFV600E kinase inhibitor‒acquired resistance is still poorly understood. In this study, we reported that the molecular signatures of angiogenesis were enriched in early on-treated biopsies but not in disease-progressed biopsies. The process of drug resistance development was accompanied by the remodeling of vascular morphology, which was potentially manipulated by tumor-secreted proangiogenic factors. Further transcriptomic dissection indicated that tumor-secreted IGF1 drove the vascular remodeling by activating the IGF1/IGF1R axis on endothelial cells and sustained the prompt regrowth of resistant tumor. Blockade of IGF1R with small molecules at an early stage of response disrupted vascular reconstruction and subsequently delayed tumor relapse. Our findings not only showed the correlation between IGF1-mediated tumor vascular remodeling and the development of acquired resistance to BRAFV600E kinase inhibitor but also provided a potential therapeutic strategy for the prevention of tumor relapse in clinical application.