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Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omic analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omic approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions. [ABSTRACT FROM AUTHOR]

Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omic analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omic approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.

The goal of the study was to explore the mechanism underlying the progression from abnormal uterine bleeding with ovulatory dysfunction (AUB-O) to AUB with atypical hyperplasia/malignancy (AUB-M). AUB-O, AUB-M and control endometrial tissues were subjected to multi-omic analyses to identify biomarkers. Differentially expressed genes (DEGs) and differentially expressed proteins (DEPs), including SFRP4, between the AUB-O and AUB-M groups were identified. The expression of SFRP4 was upregulated in endometrial tissues from AUB-O groups compared to that from AUB-M groups. SFRP4 knockdown in human endometrial epithelial cells (hEECs) promoted cell migration, invasion, proliferation and colony formation but inhibited apoptosis. Furthermore, the levels of key Wnt pathway proteins were altered by SFRP4 knockdown: Wnt-5A was downregulated and Wnt-7A was upregulated. In conclusion, we identified SFRP4 as an AUB-O-related molecule. SFRP4 might play a key role in hEECs apoptosis, migration, invasion, proliferation and colony formation via the Wnt pathway. SFRP4 may serve as a repressive factor regarding the progression of AUB-O to AUB-M. However, further studies are warranted to elucidate the exact mechanism.

This document is an editorial published in Frontiers in Plant Science titled "Genome and metagenome interactions in the plant holobiont for plant health and productivity." The editorial discusses the importance of plant-microbe interactions in both natural and agricultural environments. It highlights the impact of these interactions on plant health, resilience, and productivity, and explores topics such as the plant microbiota, sustainable agricultural practices, and the use of microbes as biofertilizers. The editorial also raises questions about the significance of microbial dynamics and the potential benefits of plant-microbe symbiosis. Overall, the document provides insights into the complex and dynamic relationships between plants and their microbiota. [Extracted from the article]

This article explores the importance of integration in systems biology and medicine research, highlighting the need to address fragmentation and biases in the field. It emphasizes the significance of considering the exposome, which encompasses cognitive, behavioral, and environmental factors, in research. The article identifies challenges in implementing systems biology and medicine approaches, such as cost, participant burden, imprecise measures, limited cross-disciplinary implementation of analytical tools, and regulatory barriers. It concludes by emphasizing the need for a paradigm shift in healthcare and the translation of scientific discoveries into clinical care. The document also includes references and acknowledgments related to systems biology and systems medicine, covering topics such as social environments and health, wearable devices, bias in research, stress management, and the impact of genetics and the environment on health. [Extracted from the article]

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals.

Electronic cigarette (Ecig) use has become more common, gaining increasing acceptance as a safer alternative to tobacco smoking. However, the 2019 outbreak of Ecig and Vaping-Associated Lung Injury (EVALI) alerted the community to the potential for incorporation of deleterious ingredients such as vitamin E acetate into products without adequate safety testing. Understanding Ecig induced molecular changes in the lung and systemically can provide a path to safety assessment and protect consumers from unsafe formulations. While vitamin E acetate has been largely removed from commercial and illicit products, many Ecig products contain additives that remain largely uncharacterized. In this study, we determined the lung-specific effects as well as systemic immune effects in response to exposure to a common Ecig base, propylene glycol and vegetable glycerin (PGVG), with and without a 1% addition of phytol, a diterpene alcohol that has been found in commercial products. We exposed animals to PGVG with and without phytol and assessed metabolite, lipid, and transcriptional markers in the lung. We found both lung-specific as well as systemic effects in immune parameters, metabolites, and lipids. Phytol drove modest changes in lung function and increased splenic CD4 T cell populations. We also conducted multi-omic data integration to better understand early complex pulmonary responses, highlighting a central enhancement of acetylcholine responses and downregulation of palmitic acid connected with conventional flow cytometric assessments of lung, systemic inflammation, and pulmonary function. Our results demonstrate that Ecig exposure not only leads to changes in pulmonary function but also affects systemic immune and metabolic parameters. [ABSTRACT FROM AUTHOR]

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort. [ABSTRACT FROM AUTHOR]

The Parkinson’s Progression Markers Initiative (PPMI) has collected more than a decade’s worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and ‘omics’ biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.

Metabolic status and gutmicroecology are implicated in psoriasis. Methotrexate (MTX) is usually the first-line treatment for this disease. However, the relationship between MTX and host metabolic status and the gut microbiota is unclear. This study aimed to characterize the features of blood metabolome and gut microbiome in patients with psoriasis after treatment with MTX. Serum and stool samples were collected from 15 patients with psoriasis. Untargeted liquid chromatography--mass spectrometry and metagenomics sequencing were applied to profile the blood metabolome and gut microbiome, respectively. We found that the response to MTX varied according to metabolomic and metagenomic features at baseline; for example, patients who had high levels of serumnutrientmolecular andmore enriched gut microbiota had a poor response. After 16 weeks of MTX, we observed a reduction in microbial activity pathways, and patients with a good response showedmoremicrobial activity and less biosynthesis of serum fatty acid. We also found an association between the serum metabolome and the gut microbiome before intervention with MTX. Carbohydrate metabolism, transporter systems, and protein synthesis within microbes were associated with host metabolic clusters of lipids, benzenoids, and organic acids. These findings suggest that the metabolic status of the blood and the gut microbiome is involved in the effectiveness ofMTX in psoriasis, and that inhibition of symbiotic intestinal microbiota may be one of the mechanisms of action of MTX. Prospective studies in larger sample sizes are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

With continued advances in cancer research, the crucial role of the tumor microenvironment (TME) in regulating tumor progression and influencing immunotherapy outcomes has been realized over the years. A series of studies devoted to enhancing the response to immunotherapies through exploring efficient predictive biomarkers and new combination approaches. The microfluidic technology not only promoted the development of multiomics analyses but also enabled the recapitulation of TME in vitro microfluidic system, which made these devices attractive across studies for optimization of immunotherapy. Here, we reviewed the application of microfluidic systems in modeling TME and the potential of these devices in predicting and monitoring immunotherapy effects. [ABSTRACT FROM AUTHOR]

The goal of the study was to explore the mechanism underlying the progression from abnormal uterine bleeding with ovulatory dysfunction (AUB-O) to AUB with atypical hyperplasia/malignancy (AUB-M). AUB-O, AUB-M and control endometrial tissues were subjected to multi-omic analyses to identify biomarkers. Differentially expressed genes (DEGs) and differentially expressed proteins (DEPs), including SFRP4, between the AUB-O and AUB-M groups were identified. The expression of SFRP4 was upregulated in endometrial tissues from AUB-O groups compared to that from AUB-M groups. SFRP4 knockdown in human endometrial epithelial cells (hEECs) promoted cell migration, invasion, proliferation and colony formation but inhibited apoptosis. Furthermore, the levels of key Wnt pathway proteins were altered by SFRP4 knockdown: Wnt-5A was downregulated and Wnt-7A was upregulated. In conclusion, we identified SFRP4 as an AUB-O-related molecule. SFRP4 might play a key role in hEECs apoptosis, migration, invasion, proliferation and colony formation via the Wnt pathway. SFRP4 may serve as a repressive factor regarding the progression of AUB-O to AUB-M. However, further studies are warranted to elucidate the exact mechanism., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration. [ABSTRACT FROM AUTHOR]

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals.

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

Systemic sclerosis (SSc), a complex multi-systemic disease characterized by immune dysregulation, vasculopathy and fibrosis, is associated with high mortality. Its pathogenesis is only partially understood. The heterogenous pathological processes that define SSc and its stages present a challenge to targeting appropriate treatment, with differing treatment outcomes of SSc patients despite similar initial clinical presentations. Timing of the appropriate treatments targeted at the underlying disease process is critical. For example, immunomodulatory treatments may be used for patients in a predominantly inflammatory phase, anti-fibrotic treatments for those in the fibrotic phase, or combination therapies for those in the fibro-inflammatory phase. In advancing personalized care through precision medicine, groups of patients with similar disease characteristics and shared pathological processes may be identified through molecular stratification. This would improve current clinical sub-setting systems and guide personalization of therapies. In this review, we will provide updates in SSc clinical and molecular stratification in relation to patient outcomes and treatment responses. Promises of molecular stratification through advances in high-dimensional tools, including omic-based stratification (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics) and machine learning will be discussed. Innovative and more granular stratification systems that integrate molecular characteristics to clinical phenotypes would potentially improve therapeutic approaches through personalized medicine and lead to better patient outcomes.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.1

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATeg R a Bioconductor package.1 [ABSTRACT FROM AUTHOR]

The human microbiome and its metabolic output play an important role in shaping human health and disease. The host-microbe crosstalk is often characterised using metabolomics and metagenomics, yet a large proportion of microbial metabolites and the molecular functions of gut microbes remain unknown. This review summarises the strategies for annotation and discovery of the novel metabolites from the human gut microbiome, either using metabolomics alone or in combination with metagenomics, and presents data analysis methods for combining these two types of data to obtain biological insights. Applications of the gut microbiome research in biomarker screening, precision medicine, microbiome medicine, and drug discovery are also discussed, along with the perspectives, challenges and limitations of this research field. • Metabolomics and metagenomics are used for investigating the gut microbiome. • Comprehensive characterizations are performed in gut microbiome studies. • Many metabolites and microbes remain undetected with current analytical practices. • We reviewed the current practices for metagenomics and metabolomics data integration. • The applications, challenges and perspectives of gut research are summarised. [ABSTRACT FROM AUTHOR]

The anaerobic bacterium Propionibacterium acnes is believed to play an important role in the pathophysiology of the common skin disease acne vulgaris. Over the last 10 years our understanding of the taxonomic and intraspecies diversity of this bacterium has increased tremendously, and with it the realisation that particular strains are associated with skin health while others appear related to disease. This extensive review will cover our current knowledge regarding the association of P. acnes phylogroups, clonal complexes and sequence types with acne vulgaris based on multilocus sequence typing of isolates, and direct ribotyping of the P. acnes strain population in skin microbiome samples based on 16S rDNA metagenomic data. We will also consider how multi-omic and biochemical studies have facilitated our understanding of P. acnes pathogenicity and interactions with the host, thus providing insights into why certain lineages appear to have a heightened capacity to contribute to acne vulgaris development, while others are positively associated with skin health. We conclude with a discussion of new therapeutic strategies that are currently under investigation for acne vulgaris, including vaccination, and consider the potential of these treatments to also perturb beneficial lineages of P. acnes on the skin.

With continued advances in cancer research, the crucial role of the tumor microenvironment (TME) in regulating tumor progression and influencing immunotherapy outcomes has been realized over the years. A series of studies devoted to enhancing the response to immunotherapies through exploring efficient predictive biomarkers and new combination approaches. The microfluidic technology not only promoted the development of multi-omics analyses but also enabled the recapitulation of TME in vitro microfluidic system, which made these devices attractive across studies for optimization of immunotherapy. Here, we reviewed the application of microfluidic systems in modeling TME and the potential of these devices in predicting and monitoring immunotherapy effects., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Fan, Ding, Xu, Liu and Wang.)

Metabolic status and gut microecology are implicated in psoriasis. Methotrexate (MTX) is usually the first-line treatment for this disease. However, the relationship between MTX and host metabolic status and the gut microbiota is unclear. This study aimed to characterize the features of blood metabolome and gut microbiome in patients with psoriasis after treatment with MTX. Serum and stool samples were collected from 15 patients with psoriasis. Untargeted liquid chromatography-mass spectrometry and metagenomics sequencing were applied to profile the blood metabolome and gut microbiome, respectively. We found that the response to MTX varied according to metabolomic and metagenomic features at baseline; for example, patients who had high levels of serum nutrient molecular and more enriched gut microbiota had a poor response. After 16 weeks of MTX, we observed a reduction in microbial activity pathways, and patients with a good response showed more microbial activity and less biosynthesis of serum fatty acid. We also found an association between the serum metabolome and the gut microbiome before intervention with MTX. Carbohydrate metabolism, transporter systems, and protein synthesis within microbes were associated with host metabolic clusters of lipids, benzenoids, and organic acids. These findings suggest that the metabolic status of the blood and the gut microbiome is involved in the effectiveness of MTX in psoriasis, and that inhibition of symbiotic intestinal microbiota may be one of the mechanisms of action of MTX. Prospective studies in larger sample sizes are needed to confirm these findings., Competing Interests: Author LH was employed by Kangmeihuada GeneTech Co., Ltd. Authors BR and XF were employed by BGI-Shenzhen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Qiu, Deng, Deng, Yao, Yan, Ye, Shang, Deng, Han, Zheng, Roy, Chen, Han, Huang, Fang and Lu.)

Systemic sclerosis (SSc), a complex multi-systemic disease characterized by immune dysregulation, vasculopathy and fibrosis, is associated with high mortality. Its pathogenesis is only partially understood. The heterogenous pathological processes that define SSc and its stages present a challenge to targeting appropriate treatment, with differing treatment outcomes of SSc patients despite similar initial clinical presentations. Timing of the appropriate treatments targeted at the underlying disease process is critical. For example, immunomodulatory treatments may be used for patients in a predominantly inflammatory phase, anti-fibrotic treatments for those in the fibrotic phase, or combination therapies for those in the fibro-inflammatory phase. In advancing personalized care through precision medicine, groups of patients with similar disease characteristics and shared pathological processes may be identified through molecular stratification. This would improve current clinical sub-setting systems and guide personalization of therapies. In this review, we will provide updates in SSc clinical and molecular stratification in relation to patient outcomes and treatment responses. Promises of molecular stratification through advances in high-dimensional tools, including omic-based stratification (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics) and machine learning will be discussed. Innovative and more granular stratification systems that integrate molecular characteristics to clinical phenotypes would potentially improve therapeutic approaches through personalized medicine and lead to better patient outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Noviani, Chellamuthu, Albani and Low.)

The anaerobic bacterium Propionibacterium acnes is believed to play an important role in the pathophysiology of the common skin disease acne vulgaris. Over the last 10 years our understanding of the taxonomic and intraspecies diversity of this bacterium has increased tremendously, and with it the realisation that particular strains are associated with skin health while others appear related to disease. This extensive review will cover our current knowledge regarding the association of P. acnes phylogroups, clonal complexes and sequence types with acne vulgaris based on multilocus sequence typing of isolates, and direct ribotyping of the P. acnes strain population in skin microbiome samples based on 16S rDNA metagenomic data. We will also consider how multi-omic and biochemical studies have facilitated our understanding of P. acnes pathogenicity and interactions with the host, thus providing insights into why certain lineages appear to have a heightened capacity to contribute to acne vulgaris development, while others are positively associated with skin health. We conclude with a discussion of new therapeutic strategies that are currently under investigation for acne vulgaris, including vaccination, and consider the potential of these treatments to also perturb beneficial lineages of P. acnes on the skin. [ABSTRACT FROM AUTHOR]