Enhancement of corn stover conversion to carboxylates by extrusion and biotic triggers in solid-state fermentation.

Solid-state fermentation is a potential technology for developing lignocellulosic biomass-based biorefineries. This work dealt with solid-state fermentation for carboxylates production from corn stover, as building blocks for a lignocellulosic feedstock-based biorefinery. The effect of extrusion pretreatment, together with the action of a microbial consortia and hydrolytic enzymes as biotic triggers, was investigated on corn stover conversion, microbial metabolic pathways, and populations. The extrusion caused changes in the physical and morphological characteristics, without altering the biochemical composition of the corn stover. Extrusion also led to remarkable differences in the composition of the indigenous microbial population of the substrate. Consequently, it affected the structure of community developed after fermentation and the substrate conversion yield, which increased by 118% (from 23 ± 4 gCOD/kgVSi obtained with raw substrate to 51 ± 1 gCOD/kgVSi with extruded corn stover) with regard to self-fermentation experiments. The use of activated sludge as inoculum further increased the total substrate conversion into carboxylates, up to 60 ± 2 gCOD/kgVSi, and shaped the microbial communities (mainly composed of bacteria from the Clostridia and Bacteroidia classes) with subsequent homogenization of the fermentation pathways. The addition of hydrolytic enzymes into the reactors further increased the corn stover conversion, leading to a maximum yield of 142 ± 1 gCOD/kgVSi. Thus, extrusion pretreatment combined with the use of an inoculum and enzyme addition increased by 506% corn stover conversion into carboxylates. Beside biomass pretreatment, the results of this study indicated that biotic factor greatly impacted solid-state fermentation by shaping the microbial communities and related metabolic pathways.

MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides.

Cancer-associated immune dysfunction is a major challenge for effective therapies. The emergence of antibodies targeting tumor cell-surface antigens led to advancements in the treatment of hematopoietic malignancies, particularly blood cancers. Yet their impact is constrained against tumors of hematopoietic origin manifesting in the skin. In this study, we employ a clonality-supervised deep learning methodology to dissect key pathological features implicated in mycosis fungoides, the most common cutaneous T-cell lymphoma. Our investigations unveil the prominence of the IL-32ß-major histocompatibility complex (MHC)-I axis as a critical determinant in tumor T-cell immune evasion within the skin microenvironment. In patients' skin, we find MHC-I to detrimentally impact the functionality of natural killer (NK) cells, diminishing antibody-dependent cellular cytotoxicity and promoting resistance of tumor skin T-cells to cell-surface targeting therapies. Through murine experiments in female mice, we demonstrate that disruption of the MHC-I interaction with NK cell inhibitory Ly49 receptors restores NK cell anti-tumor activity and targeted T-cell lymphoma elimination in vivo. These findings underscore the significance of attenuating the MHC-I-dependent immunosuppressive networks within skin tumors. Overall, our study introduces a strategy to reinvigorate NK cell-mediated anti-tumor responses to overcome treatment resistance to existing cell-surface targeted therapies for skin lymphoma.

A benchmark of computational pipelines for single-cell histone modification data.

BACKGROUND: Single-cell histone post translational modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues and are likely to unlock our understanding of various mechanisms involved in development or diseases. Running scHTPM experiments and analyzing the data produced remains challenging since few consensus guidelines currently exist regarding good practices for experimental design and data analysis pipelines. RESULTS: We perform a computational benchmark to assess the impact of experimental parameters and data analysis pipelines on the ability of the cell representation to recapitulate known biological similarities. We run more than ten thousand experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. This allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation and that using fixed-size bin counts outperforms annotation-based binning. Dimension reduction methods based on latent semantic indexing outperform others, and feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed. CONCLUSIONS: This benchmark provides a comprehensive study on how experimental parameters and computational choices affect the representation of single-cell HPTM data. We propose a series of recommendations regarding matrix construction, feature and cell selection, and dimensionality reduction algorithms.

Th17-associated cytokines IL-17 and IL-23 in inflamed skin of Darier disease patients as potential therapeutic targets.

Darier disease (DD) is a rare, inherited multi-organ disorder associated with mutations in the ATP2A2 gene. DD patients often have skin involvement characterized by malodorous, inflamed skin and recurrent, severe infections. Therapeutic options are limited and inadequate for the long-term management of this chronic disease. The aim of this study was to characterize the cutaneous immune infiltrate in DD skin lesions in detail and to identify new therapeutic targets. Using gene and protein expression profiling assays including scRNA sequencing, we demonstrate enhanced expression of Th17-related genes and cytokines and increased numbers of Th17 cells in six DD patients. We provide evidence that targeting the IL-17/IL-23 axis in a case series of three DD patients with monoclonal antibodies is efficacious with significant clinical improvement. As DD is a chronic, relapsing disease, our findings might pave the way toward additional options for the long-term management of skin inflammation in patients with DD.

H3K27me3 conditions chemotolerance in triple-negative breast cancer.

The persistence of cancer cells resistant to therapy remains a major clinical challenge. In triple-negative breast cancer, resistance to chemotherapy results in the highest recurrence risk among breast cancer subtypes. The drug-tolerant state seems largely defined by nongenetic features, but the underlying mechanisms are poorly understood. Here, by monitoring epigenomes, transcriptomes and lineages with single-cell resolution, we show that the repressive histone mark H3K27me3 (trimethylation of histone H3 at lysine 27) regulates cell fate at the onset of chemotherapy. We report that a persister expression program is primed with both H3K4me3 (trimethylation of histone H3 at lysine 4) and H3K27me3 in unchallenged cells, with H3K27me3 being the lock to its transcriptional activation. We further demonstrate that depleting H3K27me3 enhances the potential of cancer cells to tolerate chemotherapy. Conversely, preventing H3K27me3 demethylation simultaneously to chemotherapy inhibits the transition to a drug-tolerant state, and delays tumor recurrence in vivo. Our results highlight how chromatin landscapes shape the potential of cancer cells to respond to initial therapy.

Making use of transcription factor enrichment to identify functional microRNA-regulons.

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.

Interactive analysis of single-cell epigenomic landscapes with ChromSCape.

Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape, a user-friendly interactive Shiny/R application distributed as a Bioconductor package, that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets. Using ChromSCape, we deconvolve chromatin landscapes within the tumor micro-environment, identifying distinct H3K27me3 landscapes associated with cell identity and breast tumor subtype.