Nicotinamide adenine dinucleotide treatment confers resistance to neonatal ischemia and hypoxia: effects on neurobehavioral phenotypes

JOURNAL/nrgr/04.03/01300535-202412000-00031/figure1/v/2024-04-08T165401Z/r/image-tiff Neonatal hypoxic-ischemic brain injury is the main cause of hypoxic-ischemic encephalopathy and cerebral palsy. Currently, there are few effective clinical treatments for neonatal hypoxic-ischemic brain injury. Here, we investigated the neuroprotective and molecular mechanisms of exogenous nicotinamide adenine dinucleotide, which can protect against hypoxic injury in adulthood, in a mouse model of neonatal hypoxic-ischemic brain injury. In this study, nicotinamide adenine dinucleotide (5 mg/kg) was intraperitoneally administered 30 minutes before surgery and every 24 hours thereafter. The results showed that nicotinamide adenine dinucleotide treatment improved body weight, brain structure, adenosine triphosphate levels, oxidative damage, neurobehavioral test outcomes, and seizure threshold in experimental mice. Tandem mass tag proteomics revealed that numerous proteins were altered after nicotinamide adenine dinucleotide treatment in hypoxic-ischemic brain injury mice. Parallel reaction monitoring and western blotting confirmed changes in the expression levels of proteins including serine (or cysteine) peptidase inhibitor, clade A, member 3N, fibronectin 1, 5'-nucleotidase, cytosolic IA, microtubule associated protein 2, and complexin 2. Proteomics analyses showed that nicotinamide adenine dinucleotide ameliorated hypoxic-ischemic injury through inflammation-related signaling pathways (e.g., nuclear factor-kappa B, mitogen-activated protein kinase, and phosphatidylinositol 3 kinase/protein kinase B). These findings suggest that nicotinamide adenine dinucleotide treatment can improve neurobehavioral phenotypes in hypoxic-ischemic brain injury mice through inflammation-related pathways.

A highly efficient method for genomic deletion across diverse lengths in thermophilic Parageobacillus thermoglucosidasius

Parageobacillus thermoglucosidasius is emerging as a highly promising thermophilic organism for metabolic engineering. The utilization of CRISPR-Cas technologies has facilitated programmable genetic manipulation in P. thermoglucosidasius. However, the absence of thermostable NHEJ enzymes limited the capability of the endogenous type I CRISPR-Cas system to generate a variety of extensive genomic deletions. Here, two thermophilic NHEJ enzymes were identified and combined with the endogenous type I CRISPR-Cas system to develop a genetic manipulation tool that can achieve long-range genomic deletion across various lengths. By optimizing this tool-through adjusting the expression level of NHEJ enzymes and leveraging our discovery of a negative correlation between GC content of the guide RNA (gRNA) and deletion efficacy-we streamlined a comprehensive gRNA selection manual for whole-genome editing, achieving a 100 % success rate in randomly selecting gRNAs. Notably, using just one gRNA, we achieved genomic deletions spanning diverse length, exceeding 200 kilobases. This tool will facilitate the genomic manipulation of P. thermoglucosidasius for both fundamental research and applied engineering studies, further unlocking its potential as a thermophilic cell factory.

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

Chronic intermittent ethanol exposure-induced m6A modifications around mRNA stop codons of opioid receptor genes

Chronic alcohol consumption may alter mRNA methylation and expression levels of genes related to addiction and reward in the brain, potentially contributing to alcohol tolerance and dependence. Neuron-like (SH-SY5Y) and non-neuronal (SW620) cells were utilized as models to examine chronic intermittent ethanol (CIE) exposure-induced global m6A RNA methylation changes, as well as m6A mRNA methylation changes around the stop codon of three opioid receptor genes (OPRM1, OPRD1, and OPRK1), which are known to regulate pain, reward, and addiction behaviours. CIE exposure for three weeks significantly increased global RNA methylation levels in both SH-SY5Y (t = 3.98, P = 0.007) and SW620 (t = 2.24, P = 0.067) cells. However, a 3-week CIE exposure resulted in hypomethylation around mRNA stop codon regions of OPRM1 and OPRD1 in both cell lines [OPRM1(SH-SY5Y): t = -5.05, P = 0.0005; OPRM1(SW620): t = -3.19, P = 0.013; OPRD1(SH-SY5Y): t = -13.43, P < 0.00001; OPRD1(SW620): t = -4.00, P = 0.003]. Additionally, mRNA expression levels of OPRM1, OPRD1, and OPRK1 were downregulated (corresponding to mRNA hypomethylation) in both SH-SY5Y and SW620 cells after a 3-week CIE exposure. The present study demonstrated that chronic ethanol exposure altered global RNA methylation levels, as well as mRNA methylation and expression levels of opioid receptor genes in both neuron-like and non-neuronal cells. Our findings suggest a potential epitranscriptomic mechanism by which chronic alcohol consumption remodels the expression of reward-related and alcohol responsive genes in the brain, thus increasing the risk of alcohol use disorder development.Abbreviations: OPRM1: the μ-opioid receptor; OPRD1: the δ-opioid receptor; OPRK1: the κ-opioid receptor; CIE: chronic intermittent ethanol exposure; CIE+WD: chronic intermittent ethanol exposure followed by a 24-hr withdrawal; SH-SY5Y: human neuroblastoma cell Line; SW620: human colon carcinoma cell line; RT-qPCR: reverse transcription followed by quantitative polymerase reaction; MazF-RT-qPCR: MazF digestion followed by RT-qPCR.

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden. Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour. Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

Cysteine hyperoxidation rewires communication pathways in the nucleosome and destabilizes the dyad

Gene activity is tightly controlled by reversible chemical modifications called epigenetic marks, which are of various types and modulate gene accessibility without affecting the DNA sequence. Despite an increasing body of evidence demonstrating the role of oxidative-type modifications of histones in gene expression regulation, there remains a complete absence of structural data at the atomistic level to understand the molecular mechanisms behind their regulatory action. Owing to μs time-scale MD simulations and protein communication networks analysis, we describe the impact of histone H3 hyperoxidation (i.e., S-sulfonylation) on the nucleosome core particle dynamics. Our results reveal the atomic-scale details of the intrinsic structural networks within the canonical histone core and their perturbation by hyperoxidation of the histone H3 C110. We show that this modification involves local rearrangements of the communication networks and destabilizes the dyad, and that one modification is enough to induce a maximal structural signature. Our results suggest that cysteine hyperoxidation in the nucleosome core particle might favor its disassembly.

Effects of age on differential resistance to duck hepatitis A virus genotype 3 in Pekin ducks by 16 S and transcriptomics

Duck hepatitis A virus genotype 3 (DHAV-3) is the major cause of viral hepatitis in ducks in Asia. Previous studies have shown that ducklings younger than 21 days are more susceptible to DHAV-3. To elucidate the mechanism by which age affects the differential susceptibility of Pekin ducks to DHAV-3, intestinal (n = 520), liver (n = 40) and blood (n = 260) samples were collected from control and DHAV-3-infected ducks at 7, 10, 14, and 21 days of age. Comparisons of plasma markers, mortality rates, and intestinal histopathological data showed that the resistance of Pekin ducks to DHAV-3 varied with age. 16 S sequencing revealed that the ileal microbial composition was influenced by age, and this correlation was greater than that recorded for caecal microbes. Candidatus Arthromitus, Bacteroides, Corynebacterium, Enterococcus, Romboutsia, and Streptococcus were the differntially abundant microbes in the ileum at the genus level after DHAV-3 infection and were significantly correlated with 7 differentially expressed genes (DEGs) in 7- and 21-day-old ducklings. 3 immunity-related pathways were significantly different between 7- and 21-day-old ducklings, especially for IFIH1-mediated induction of the interferon-alpha/beta pathway, which induces differential production of CD8(+) T cells and was influenced by a combination of differentially abundant microbiota and DEGs. We found that microbes in the ileum changed regularly with age. The intestinal microbiota was associated with the expression of genes in the liver through IFIH1-mediated induction of the interferon-alpha/beta pathway, which may partially explain why younger ducklings were more susceptible to DHAV-3 infection.

Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge

ABSTRACTImproved sanitation, increased access to health care, and advances in preventive and clinical medicine have reduced the mortality and morbidity rates of several infectious diseases. However, recent outbreaks of several emerging infectious diseases (EIDs) have caused substantial mortality and morbidity, and the frequency of these outbreaks is likely to increase due to pathogen, environmental, and population effects driven by climate change. Extreme or persistent changes in temperature, precipitation, humidity, and air pollution associated with climate change can, for example, expand the size of EID reservoirs, increase host-pathogen and cross-species host contacts to promote transmission or spillover events, and degrade the overall health of susceptible host populations leading to new EID outbreaks. It is therefore vital to establish global strategies to track and model potential responses of candidate EIDs to project their future behaviour and guide research efforts on early detection and diagnosis technologies and vaccine development efforts for these targets. Multi-disciplinary collaborations are demanding to develop effective inter-continental surveillance and modelling platforms that employ artificial intelligence to mitigate climate change effects on EID outbreaks. In this review, we discuss how climate change has increased the risk of EIDs and describe novel approaches to improve surveillance of emerging pathogens that pose the risk for EID outbreaks, new and existing measures that could be used to contain or reduce the risk of future EID outbreaks, and new methods to improve EID tracking during further outbreaks to limit disease transmission.

Sculpting nuclear envelope identity from the endoplasmic reticulum during the cell cycle

The nuclear envelope (NE) regulates nuclear functions, including transcription, nucleocytoplasmic transport, and protein quality control. While the outer membrane of the NE is directly continuous with the endoplasmic reticulum (ER), the NE has an overall distinct protein composition from the ER, which is crucial for its functions. During open mitosis in higher eukaryotes, the NE disassembles during mitotic entry and then reforms as a functional territory at the end of mitosis to reestablish nucleocytoplasmic compartmentalization. In this review, we examine the known mechanisms by which the functional NE reconstitutes from the mitotic ER in the continuous ER-NE endomembrane system during open mitosis. Furthermore, based on recent findings indicating that the NE possesses unique lipid metabolism and quality control mechanisms distinct from those of the ER, we explore the maintenance of NE identity and homeostasis during interphase. We also highlight the potential significance of membrane junctions between the ER and NE.

Context-dependent genomic locus effects on antibody production in recombinant Chinese hamster ovary cells generated through random integration

High-yield production of therapeutic protein using Chinese hamster ovary (CHO) cells requires stable cell line development (CLD). CLD typically uses random integration of transgenes; however, this results in clonal variation and subsequent laborious clone screening. Therefore, site-specific integration of a protein expression cassette into a desired chromosomal locus showing high transcriptional activity and stability, referred to as a hot spot, is emerging. Although positional effects are important for therapeutic protein expression, the sequence-specific mechanisms by which hotspots work are not well understood. In this study, we performed whole-genome sequencing (WGS) to locate randomly inserted vectors in the genome of recombinant CHO cells expressing high levels of monoclonal antibodies (mAbs) and experimentally validated these locations and vector compositions. The integration site was characterized by active histone marks and potential enhancer activities, and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) mediated indel mutations in the region upstream of the integration site led to a significant reduction in specific antibody productivity by up to 30%. Notably, the integration site and its core region did not function equivalently outside the native genomic context, showing a minimal effect on the increase in exogenous protein expression in the host cell line. We also observed a superior production capacity of the mAb expressing cell line compared to that of the host cell line. Collectively, this study demonstrates that developing recombinant CHO cell lines to produce therapeutic proteins at high levels requires a balance of factors including transgene configuration, genomic locus landscape, and host cell properties.

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: β-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

The astrocyte-enriched gene deathstar plays a crucial role in the development, locomotion, and lifespan of D. melanogaster

The Drosophila melanogaster brain is a complex organ with various cell types, orchestrating the development, physiology, and behaviors of the fly. While each cell type in Drosophila brain is known to express a unique gene set, their complete genetic profile is still unknown. Advances in the RNA sequencing techniques at single-cell resolution facilitate identifying novel cell type markers and/or re-examining the specificity of the available ones. In this study, exploiting a single-cell RNA sequencing data of Drosophila optic lobe, we categorized the cells based on their expression pattern for known markers, then the genes with enriched expression in astrocytes were identified. CG11000 was identified as a gene with a comparable expression profile to the Eaat1 gene, an astrocyte marker, in every individual cell inside the Drosophila optic lobe and midbrain, as well as in the entire Drosophila brain throughout its development. Consistent with our bioinformatics data, immunostaining of the brains dissected from transgenic adult flies showed co-expression of CG11000 with Eaat1 in a set of single cells corresponding to the astrocytes in the Drosophila brain. Physiologically, inhibiting CG11000 through RNA interference disrupted the normal development of male D. melanogaster, while having no impact on females. Expression suppression of CG11000 in adult flies led to decreased locomotion activity and also shortened lifespan specifically in astrocytes, indicating the gene's significance in astrocytes. We designated this gene as 'deathstar' due to its crucial role in maintaining the star-like shape of glial cells, astrocytes, throughout their development into adult stage.

Prediction models for COVID-19 disease outcomes

SARS-CoV-2 has caused over 6.9 million deaths and continues to produce lasting health consequences. COVID-19 manifests broadly from no symptoms to death. In a retrospective cross-sectional study, we developed personalized risk assessment models that predict clinical outcomes for individuals with COVID-19 and inform targeted interventions. We sequenced viruses from SARS-CoV-2-positive nasopharyngeal swab samples between July 2020 and July 2022 from 4450 individuals in Missouri and retrieved associated disease courses, clinical history, and urban-rural classification. We integrated this data to develop machine learning-based predictive models to predict hospitalization, ICU admission, and long COVID.The mean age was 38.3 years (standard deviation = 21.4) with 55.2% (N = 2453) females and 44.8% (N = 1994) males (not reported, N = 4). Our analyses revealed a comprehensive set of predictors for each outcome, encompassing human, environment, and virus genome-wide genetic markers. Immunosuppression, cardiovascular disease, older age, cardiac, gastrointestinal, and constitutional symptoms, rural residence, and specific amino acid substitutions were associated with hospitalization. ICU admission was associated with acute respiratory distress syndrome, ventilation, bacterial co-infection, rural residence, and non-wild type SARS-CoV-2 variants. Finally, long COVID was associated with hospital admission, ventilation, and female sex.Overall, we developed risk assessment models that offer the capability to identify patients with COVID-19 necessitating enhanced monitoring or early interventions. Of importance, we demonstrate the value of including key elements of virus, host, and environmental factors to predict patient outcomes, serving as a valuable platform in the field of personalized medicine with the potential for adaptation to other infectious diseases.

N6-methyladenosine methylation analysis of long noncoding RNAs and mRNAs in 5-FU-resistant colon cancer cells

N6 methyladenosine (m6A), methylation at the sixth N atom of adenosine, is the most common and abundant modification in mammalian mRNAs and non-coding RNAs. Increasing evidence shows that the alteration of m6A modification level could regulate tumour proliferation, metastasis, self-renewal, and immune infiltration by regulating the related expression of tumour genes. However, the role of m6A modification in colorectal cancer (CRC) drug resistance is unclear. Here, MeRIP-seq and RNA-seq techniques were utilized to obtain mRNA, lncRNA expression, and their methylation profiles in 5-Fluorouracil (5-FU)-resistant colon cancer HCT-15 cells and control cells. In addition, we performed detailed bioinformatics analysis as well as in vitro experiments of lncRNA to explore the function of lncRNA with differential m6A in CRC progression and drug resistance. In this study, we obtained the m6A methylomic landscape of CRC cells and resistance group cells by MeRIP-seq and RNA-seq. We identified 3698 differential m6A peaks, of which 2224 were hypermethylated, and 1474 were hypomethylated. Among the lncRNAs, 60 were hypermethylated, and 38 were hypomethylated. GO and KEGG analysis annotations showed significant enrichment of endocytosis and MAPK signalling pathways. Moreover, knockdown of lncRNA ADIRF-AS1 and AL139035.1 promoted CRC proliferation and invasive metastasis in vitro. lncRNA- mRNA network showed that ADIRF-AS1 and AL139035.1 May play a key role in regulating drug resistance formation. We provide the first m6A methylation profile in 5-FU resistance CRC cells and analyse the functions of differential m6A-modified mRNAs and lncRNAs. Our results indicated that differential m6A RNAs were significantly associated with MAPK signalling and endocytosis after induction of 5-FU resistance. Knockdown of LncRNA ADIRF-AS1 and AL139035.1 promotes CRC progression and might be critical in regulating drug resistance formation.

DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model

Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pre-trained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the model's effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp).

Differences in oxazolidinone resistance mechanisms and small colony variants emergence of Staphylococcus aureus induced in an in vitro resistance development model

Invasive Staphylococcus aureus infections are associated with a high burden of disease, case fatality rate and healthcare costs. Oxazolidinones such as linezolid and tedizolid are considered potential treatment choices for conditions involving methicillin resistance or penicillin allergies. Additionally, they are being investigated as potential inhibitors of toxins in toxin-mediated diseases. In this study, linezolid and tedizolid were evaluated in an in vitro resistance development model for induction of resistance in S. aureus. Whole genome sequencing was conducted to elucidate resistance mechanisms through the identification of causal mutations. After inducing resistance to both linezolid and tedizolid, several partially novel single nucleotide variants (SNVs) were detected in the rplC gene, which encodes the 50S ribosome protein L3 in S. aureus. These SNVs were found to decrease the binding affinity, potentially serving as the underlying cause for oxazolidinone resistance. Furthermore, in opposite to linezolid we were able to induce phenotypically small colony variants of S. aureus after induction of resistance with tedizolid for the first time in literature. In summary, even if different antibiotic concentrations were required and SNVs were detected, the principal capacity of S. aureus to develop resistance to oxazolidinones seems to differ between linezolid and tedizolid in-vivo but not in vitro. Stepwise induction of resistance seems to be a time and cost-effective tool for assessing resistance evolution. Inducted-resistant strains should be examined and documented for epidemiological reasons, if MICs start to rise or oxazolidinone-resistant S. aureus outbreaks become more frequent.

Characterization of Tau95 led to the identification of a four-subunit TFIIIC complex in trypanosomatid parasites

RNA polymerase III (RNAP III) synthetizes small essential non-coding RNA molecules such as tRNAs and 5S rRNA. In yeast and vertebrates, RNAP III needs general transcription factors TFIIIA, TFIIIB, and TFIIIC to initiate transcription. TFIIIC, composed of six subunits, binds to internal promoter elements in RNAP III-dependent genes. Limited information is available about RNAP III transcription in the trypanosomatid protozoa Trypanosoma brucei and Leishmania major, which diverged early from the eukaryotic lineage. Analyses of the first published draft of the trypanosomatid genome sequences failed to recognize orthologs of any of the TFIIIC subunits, suggesting that this transcription factor is absent in these parasites. However, a putative TFIIIC subunit was recently annotated in the databases. Here we characterize this subunit in T. brucei and L. major and demonstrate that it corresponds to Tau95. In silico analyses showed that both proteins possess the typical Tau95 sequences: the DNA binding region and the dimerization domain. As anticipated for a transcription factor, Tau95 localized to the nucleus in insect forms of both parasites. Chromatin immunoprecipitation (ChIP) assays demonstrated that Tau95 binds to tRNA and U2 snRNA genes in T. brucei. Remarkably, by performing tandem affinity purifications we identified orthologs of TFIIIC subunits Tau55, Tau131, and Tau138 in T. brucei and L. major. Thus, contrary to what was assumed, trypanosomatid parasites do possess a TFIIIC complex. Other putative interacting partners of Tau95 were identified in T. brucei and L. major. KEY POINTS: • A four-subunit TFIIIC complex is present in T. brucei and L. major • TbTau95 associates with tRNA and U2 snRNA genes • Putative interacting partners of Tau95 might include some RNAP II regulators.

Application of mesenchymal stem/stromal cells in periodontal regeneration: Opportunities and challenges

Guided tissue regeneration (GTR) has been widely used in the periodontal treatment of intrabony and furcation defects for nearly four decades. The treatment outcomes have shown effectiveness in reducing pocket depth, improving attachment gain and bone filling in periodontal tissue. Although applying GTR could reconstruct the periodontal tissue, the surgical indications are relatively narrow, and some complications and race ethic problems bring new challenges. Therefore, it is challenging to achieve a consensus concerning the clinical benefits of GTR. With the appearance of stem cell-based regenerative medicine, mesenchymal stem/stromal cells (MSCs) have been considered a promising cell resource for periodontal regeneration. In this review, we highlight preclinical and clinical periodontal regeneration using MSCs derived from distinct origins, including non-odontogenic and odontogenic tissues and induced pluripotent stem cells, and discuss the transplantation procedures, therapeutic mechanisms, and concerns to evaluate the effectiveness of MSCs.

AMWEst, a new thermostable and detergent-tolerant esterase retrieved from the Albian aquifer

A fosmid library was constructed with the metagenomic DNA from the high-temperature sediment-rich water of the Albian aquifer (Algeria). Functional screening of this library was subsequently done looking for genes encoding lipolytic enzymes. We identified a novel gene named AMWEst (1209 base pairs) encoding a protein of 402 amino acids with a predicted molecular weight of 43.44 kDa and conferring esterase activity. AMWEst was successfully overexpressed in the yeast mesophilic host Saccharomyces cerevisiae, and the expression system used proved to be efficient and produced sufficient activity for its biochemical characterization. Multiple sequence alignment indicated that AMWEst contained a conserved pentapeptide motif (Gly120-His121-Ser122-Gln123-Gly124). The optimum pH and temperature of the recombinant esterase AMWEst were 8 and 80 °C, respectively. Additionally, AMWEst showed higher activity towards short carbon substrates and showed maximum activity for p-nitrophenyl hexanoate (C6). Notably, AMWEst has a remarkable thermostability, and the enzyme retains almost maximum activity at 70 °C after incubation for 1 h. Moreover, enzyme activity was enhanced by high concentrations of SDS and Triton X-100 detergents. KEY POINTS: • A novel thermostable esterase has been retrieved through functional metagenomics • The esterase is detergent-tolerant, which is attractive for some applications • The esterase can be expressed in a yeast mesophilic host to enhance its yield.

NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search

The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.

Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation?

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.

Genetic confounds of transgenerational epigenetic inheritance in mice

Transgenerational epigenetic inheritance in mammals remains a controversial phenomenon. A recent study by Takahashi et al. provides evidence for this mode of inheritance in mice by using a CRISPR/Cas9-based epigenetic editing technique to modify DNA methylation levels at specific promoters and then demonstrating the inheritance of the gain in methylation in offspring. In this technical commentary, we argue that the method used in the original study inherently amplifies the likelihood of genetic changes that thereafter lead to the heritability of epigenetic changes. We provide evidence that genetic changes from multiple sources do indeed occur in these experiments and explore several avenues by which these changes could be causal to the apparent inheritance of epigenetic changes. We conclude a genetic basis of inheritance cannot be ruled out and thus transgenerational epigenetic inheritance has not been adequately established by the original study.

A novel bispecific antibody targeting two overlapping epitopes in RBD improves neutralizing potency and breadth against SARS-CoV-2

SARS-CoV-2 has been evolving into a large number of variants, including the highly pathogenic Delta variant, and the currently prevalent Omicron subvariants with extensive evasion capability, which raises an urgent need to develop new broad-spectrum neutralizing antibodies. Herein, we engineer two IgG-(scFv)2 form bispecific antibodies with overlapping epitopes (bsAb1) or non-overlapping epitopes (bsAb2). Both bsAbs are significantly superior to the parental monoclonal antibodies in terms of their antigen-binding and virus-neutralizing activities against all tested circulating SARS-CoV-2 variants including currently dominant JN.1. The bsAb1 can efficiently neutralize all variants insensitive to parental monoclonal antibodies or the cocktail with IC50 lower than 20 ng/mL, even slightly better than bsAb2. Furthermore, the cryo-EM structures of bsAb1 in complex with the Omicron spike protein revealed that bsAb1 with overlapping epitopes effectively locked the S protein, which accounts for its conserved neutralization against Omicron variants. The bispecific antibody strategy engineered from overlapping epitopes provides a novel solution for dealing with viral immune evasion.

Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis

Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFNγ and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.

Lassa virus in novel hosts: insights into the epidemiology of lassa virus infections in southern Nigeria

Identification of the diverse animal hosts responsible for spill-over events from animals to humans is crucial for comprehending the transmission patterns of emerging infectious diseases, which pose significant public health risks. To better characterize potential animal hosts of Lassa virus (LASV), we assessed domestic and non-domestic animals from 2021-2022 in four locations in southern Nigeria with reported cases of Lassa fever (LF). Birds, lizards, and domestic mammals (dogs, pigs, cattle and goats) were screened using RT-qPCR, and whole genome sequencing was performed for lineage identification on selected LASV positive samples. Animals were also screened for exposure to LASV by enzyme-linked immunosorbent assay (ELISA). Among these animals, lizards had the highest positivity rate by PCR. Genomic sequencing of samples in most infected animals showed sub-lineage 2 g of LASV. Seropositivity was highest among cattle and lowest in pigs. Though the specific impact these additional hosts may have in the broader virus-host context are still unknown - specifically relating to pathogen diversity, evolution, and transmission - the detection of LASV in non-rodent hosts living in proximity to confirmed human LF cases suggests their involvement during transmission as potential reservoirs. Additional epidemiological data comparing viral genomes from humans and animals, as well as those circulating within the environment will be critical in understanding LASV transmission dynamics and will ultimately guide the development of countermeasures for this zoonotic health threat.

scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

Using methylation data to improve transcription factor binding prediction

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

Role of folic acid in regulating gut microbiota and short-chain fatty acids based on an in vitro fermentation model

Folic acid deficiency is common worldwide and is linked to an imbalance in gut microbiota. However, based on model animals used to study the utilization of folic acid by gut microbes, there are challenges of reproducibility and individual differences. In this study, an in vitro fecal slurry culture model of folic acid deficiency was established to investigate the effects of supplementation with 5-methyltetrahydrofolate (MTHF) and non-reduced folic acid (FA) on the modulation of gut microbiota. 16S rRNA sequencing results revealed that both FA (29.7%) and MTHF (27.9%) supplementation significantly reduced the relative abundance of Bacteroidetes compared with control case (34.3%). MTHF supplementation significantly improved the relative abundance of Firmicutes by 4.49%. Notably, compared with the control case, FA and MTHF supplementation promoted an increase in fecal levels of Lactobacillus, Bifidobacterium, and Pediococcus. Short-chain fatty acid (SCFA) analysis showed that folic acid supplementation decreased acetate levels and increased fermentative production of isobutyric acid. The in vitro fecal slurry culture model developed in this study can be utilized as a model of folic acid deficiency in humans to study the gut microbiota and demonstrate that exogenous folic acid affects the composition of the gut microbiota and the level of SCFAs. KEY POINTS: • Establishment of folic acid deficiency in an in vitro culture model. • Folic acid supplementation regulates intestinal microbes and SCFAs. • Connections between microbes and SCFAs after adding folic acid are built.

scQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Structural trends in antibody-antigen binding interfaces: a computational analysis of 1833 experimentally determined 3D structures

Antibodies are attractive therapeutic candidates due to their ability to bind cognate antigens with high affinity and specificity. Still, the underlying molecular rules governing the antibody-antigen interface remain poorly understood, making in silico antibody design inherently difficult and keeping the discovery and design of novel antibodies a costly and laborious process. This study investigates the characteristics of antibody-antigen binding interfaces through a computational analysis of more than 850,000 atom-atom contacts from the largest reported set of antibody-antigen complexes with 1833 nonredundant, experimentally determined structures. The analysis compares binding characteristics of conventional antibodies and single-domain antibodies (sdAbs) targeting both protein- and peptide antigens. We find clear patterns in the number antibody-antigen contacts and amino acid frequencies in the paratope. The direct comparison of sdAbs and conventional antibodies helps elucidate the mechanisms employed by sdAbs to compensate for their smaller size and the fact that they harbor only half the number of complementarity-determining regions compared to conventional antibodies. Furthermore, we pinpoint antibody interface hotspot residues that are often found at the binding interface and the amino acid frequencies at these positions. These findings have direct potential applications in antibody engineering and the design of improved antibody libraries.

Recent advances and challenges in protein complex model accuracy estimation

Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.

SUDAZFLNC - a curated and searchable online database for zebrafish lncRNAs, mRNAs, miRNAs, and circadian expression profiles

The zebrafish (Danio rerio) has emerged as a model organism for investigating lncRNAs-driven fundamental biological processes, such as circadian rhythms, physiology, metabolism, and various diseases. While state-of-the-art sequencing technologies have identified an increasing number of lncRNAs in zebrafish, their annotations are far from complete. In this study, we collect 28,925 lncRNAs from both the published studies and our own RNA-seq analyses and establish a novel webserver-based database called SUDAZFLNC (https://sudarna.website/). The database, containing 28,925 lncRNAs, 25,432 mRNAs, and 368 miRNAs, provides several crucial features and annotations for the zebrafish RNAs, such as sequence identifiers (IDs), sequence length, hexamer score, coding probabilities, GO and KEGG annotations, and micropeptides. SUDAZFLNC also includes time-course expression profiles of 3288 lncRNAs, 25,432 mRNAs, and 342 miRNAs generated from our RNA-seq experiments, and 149, 4407, and 43 rhythmically expressed lncRNAs, mRNAs, and miRNAs, respectively. Based on the peak expression patterns, we classified these RNAs into morning RNAs, evening RNAs, and night RNAs. Users of the database can access the RNA sequences and their expression profiles by searching the corresponding IDs from the Graphical User Interface (GUI) of the database. The database supports several features to investigate RNA sequences and expression profiles, including BLAST, search of sequence and data, ID conversion, and RNA-RNA interaction prediction. This is the largest curated database of zebrafish RNAs and their expression profiles to date.

A lineage-specific protein network at the trypanosome nuclear envelope

The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization and nucleocytoplasmic transport. The molecular basis for many of these functions have diverged between eukaryotic lineages. Trypanosoma brucei, a member of the early branching eukaryotic lineage Discoba, highlights many of these, including a distinct lamina and kinetochore composition. Here, we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with S. cerevisiae and H. sapiens, and additional peripheral components of the NPC. While many of the LAPs are Kinetoplastid specific, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the trypanosome NE proteome, highlighting the diversity of nuclear biology across the eukaryotes, increasing our understanding of eukaryotic and NPC evolution.

DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data

Intronic polyadenylation (IPA) refers to a particular type of alternative polyadenylation where a gene makes use of a polyadenylation site located within its introns. Aberrant IPA events have been observed in various types of cancer. IPA can produce noncoding transcripts or truncated protein-coding transcripts with altered coding sequences in the resulting protein product. Therefore, IPA events hold the potential to act as a reservoir of tumor neoantigens. Here, we developed a computational method termed DIPAN, which incorporates IPA detection, protein fragmentation, and MHC binding prediction to predict IPA-derived neoantigens. Utilizing RNA-seq from breast cancer cell lines and ovarian cancer clinical samples, we demonstrated the significant contribution of IPA events to the neoantigen repertoire. Through mass spectrometry immunopeptidome analysis, we further illustrated the processing and presentation of IPA-derived neoantigens on the surface of cancer cells. While most IPA-derived neoantigens are sample-specific, shared neoantigens were identified in both cancer cell lines and clinical samples. Furthermore, we demonstrated an association between IPA-derived neoantigen burden and overall survival in cancer patients.

Resource: A curated database of brain-related functional gene sets (Brain.GMT)

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Integrated-omics profiling unveils the disparities of host defense to ECM scaffolds during wound healing in aged individuals

Extracellular matrix (ECM) scaffold membranes have exhibited promising potential to better the outcomes of wound healing by creating a regenerative microenvironment around. However, when compared to the application in younger individuals, the performance of the same scaffold membrane in promoting re-epithelialization and collagen deposition was observed dissatisfying in aged mice. To comprehensively explore the mechanisms underlying this age-related disparity, we conducted the integrated analysis, combing single-cell RNA sequencing (scRNA-Seq) with spatial transcriptomics, and elucidated six functionally and spatially distinctive macrophage groups and lymphocytes surrounding the ECM scaffolds. Through intergroup comparative analysis and cell-cell communication, we characterized the dysfunction of Spp1+ macrophages in aged mice impeded the activation of the type Ⅱ immune response, thus inhibiting the repair ability of epidermal cells and fibroblasts around the ECM scaffolds. These findings contribute to a deeper understanding of biomaterial applications in varied physiological contexts, thereby paving the way for the development of precision-based biomaterials tailored specifically for aged individuals in future therapeutic strategies.

MicroED structure of the C11 cysteine protease clostripain

Clostripain secreted from Clostridium histolyticum is the founding member of the C11 family of Clan CD cysteine peptidases, which is an important group of peptidases secreted by numerous bacteria. Clostripain is an arginine-specific endopeptidase. Because of its efficacy as a cysteine peptidase, it is widely used in laboratory settings. Despite its importance the structure of clostripain remains unsolved. Here we describe the first structure of an active form of C. histolyticum clostripain determined at 2.5 Å resolution using microcrystal electron diffraction (MicroED). The structure was determined from a single nanocrystal after focused ion beam milling. The structure of clostripain shows a typical Clan CD α/β/α sandwich architecture and the Cys231/His176 catalytic dyad in the active site. It has a large electronegative substrate binding pocket showing its ability to accommodate large and diverse substrates. A loop in the heavy chain formed between residues 452 and 457 is potentially important for substrate binding. In conclusion, this result demonstrates the importance of MicroED to determine the unknown structure of macromolecules such as clostripain, which can be further used as a platform to study substrate binding and design of potential inhibitors against this class of peptidases.

3D electronic implants in subretinal space: Long-term follow-up in rodents

Clinical results with photovoltaic subretinal prosthesis (PRIMA) demonstrated restoration of sight via electrical stimulation of the interneurons in degenerated retina, with resolution matching the 100 μm pixel size. Since scaling the pixels below 75 μm in the current bipolar planar geometry will significantly limit the penetration depth of the electric field and increase stimulation threshold, we explore the possibility of using smaller pixels based on a novel 3-dimensional honeycomb-shaped design. We assessed the long-term biocompatibility and stability of these arrays in rats by investigating the anatomical integration of the retina with flat and 3D implants and response to electrical stimulation over lifetime - up to 32-36 weeks post-implantation in aged rats. With both flat and 3D implants, signals elicited in the visual cortex decreased after the day of implantation by more than 3-fold, and gradually recovered over the next 12-16 weeks. With 25 μm high honeycomb walls, the majority of bipolar cells migrate into the wells, while amacrine and ganglion cells remain above the cavities, which is essential for selective network-mediated stimulation of the retina. Retinal thickness and full-field stimulation threshold with 40 μm-wide honeycomb pixels were comparable to those with planar devices - 0.05 mW/mm2 with 10 ms pulses. However, fewer cells from the inner nuclear layer migrated into the 20 μm-wide wells, and stimulation threshold increased over 12-16 weeks, before stabilizing at about 0.08 mW/mm2. Such threshold is still significantly lower than 1.8 mW/mm2 with a previous design of flat bipolar pixels, confirming the promise of the 3D honeycomb-based approach to high resolution subretinal prosthesis.

Fecal microbiota is associated with extraintestinal manifestations in inflammatory bowel disease

INTRODUCTION

A large proportion of patients with inflammatory bowel disease (IBD) experience IBD-related inflammatory conditions outside of the gastrointestinal tract, termed extraintestinal manifestations (EIMs) which further decreases quality of life and, in extreme cases, can be life threatening. The pathogenesis of EIMs remains unknown, and although gut microbiota alterations are a well-known characteristic of patients with IBD, its relationship with EIMs remains sparsely investigated. This study aimed to compare the gut microbiota of patients with IBD with and without EIMs.

METHODS

A total of 131 Danish patients with IBD were included in the study, of whom 86 had a history of EIMs (IBD-EIM) and 45 did not (IBD-C). Stool samples underwent 16S rRNA sequencing. Amplicon sequence variants (ASVs) were mapped to the Silva database. Diversity indices and distance matrices were compared between IBD-EIM and IBD-C. Differentially abundant ASVs were identified using a custom multiple model statistical analysis approach, and modules of co-associated bacteria were identified using sparse correlations for compositional data (SparCC) and related to patient EIM status.

RESULTS

Patients with IBD and EIMs exhibited increased disease activity, body mass index, increased fecal calprotectin levels and circulating monocytes and neutrophils. Microbiologically, IBD-EIM exhibited lower fecal microbial diversity than IBD-C (Mann-Whitney's test, p = .01) and distinct fecal microbiota composition (permutational multivariate analysis of variance; weighted UniFrac, R2 = 0.018, p = .01). A total of 26 ASVs exhibited differential relative abundances between IBD-EIM and IBD-C, including decreased Agathobacter and Blautia and increased Eggerthella lenta in the IBD-EIM group. SparCC analysis identified 27 bacterial co-association modules, three of which were negatively related to EIM (logistic regression, p < .05) and included important health-associated bacteria, such as Agathobacter and Faecalibacterium.

CONCLUSIONS

The fecal microbiota in IBD patients with EIMs is distinct from that in IBD patients without EIM and could be important for EIM pathogenesis.

Stress-induced epigenetic effects driven by maternal lactation in dairy cattle: a comethylation network approach

Epigenetic marks do not follow the Mendelian laws of inheritance. The environment can alter the epigenotype of an individual when exposed to different external stressors. In lactating cows, the first stages of gestation overlap with the lactation peak, creating a negative energy balance that is difficult to overcome with diet. This negative energy balance could affect early embryo development that must compete with the mammary tissue for nutrients. We hypothesize that the methylation profiles of calves born to nonlactating heifers are different from those of calves born to lactating cows. We found 50,277 differentially methylated cytosines and 2,281 differentially methylated regions between these two groups of animals. A comethylation network was constructed to study the correlation between the phenotypes of the mothers and the epigenome of the calves, revealing 265 regions associated with the phenotypes. Our study revealed the presence of DMCs and DMRs in calves gestated by heifers and lactating cows, which were linked to the dam's lactation and the calves' ICAP and milk EBV. Gene-specific analysis highlighted associations with vasculature and organ morphogenesis and cell communication and signalling. These finding support the hypothesis that calves gestated by nonlactating mothers have a different methylation profile than those gestated by lactating cows.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

A novel Slide-seq based image processing software to identify gene expression at the single cell level

Analysis of gene expression at the single-cell level could help predict the effectiveness of therapies in the field of chronic inflammatory diseases such as arthritis. Here, we demonstrate an adopted approach for processing images from the Slide-seq method. Using a puck, which consists of about 50,000 DNA barcode beads, an RNA sequence of a cell is to be read. The pucks are repeatedly brought into contact with liquids and then recorded with a conventional epifluorescence microscope. The image analysis initially consists of stitching the partial images of a sequence recording, registering images from different sequences, and finally reading out the bases. The new method enables the use of an inexpensive epifluorescence microscope instead of a confocal microscope.

WUREN: Whole-modal union representation for epitope prediction

B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Utility of artificial intelligence in a binary classification of soft tissue tumors

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Improving cardiac differentiation of human pluripotent stem cells by targeting ferroptosis

Generation of cardiomyocytes from human pluripotent stem cells (hPSCs) is of high interest for disease modelling and regenerative medicine. hPSCs can provide an unlimited source of patient-specific cardiomyocytes that are otherwise difficult to obtain from individuals. Moreover, the low proliferation rate of adult cardiomyocytes and low viability ex vivo limits the quantity of study material. Most protocols for the differentiation of cardiomyocytes from hPSCs are based on the temporal modulation of the Wnt pathway. However, during the initial stage of GSK-3 inhibition, a substantial number of cells are lost due to detachment. In this study, we aimed to increase the efficiency of generating cardiomyocytes from hPSCs. We identified cell death as a detrimental factor during this initial stage of in vitro cardiomyocyte differentiation. Through pharmacological targeting of different types of cell death, we discovered that ferroptosis was the main cell death type during the first 48 h of the in vitro differentiation procedure. Inhibiting ferroptosis using ferrostatin-1 during cardiomyocyte differentiation resulted in increased robustness and cell yield.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics. The Gra-CRC-miRTar web server can be found at: http://gra-crc-mirtar.com/.

A mini-review on gene delivery technique using nanoparticles-mediated photoporation induced by nanosecond pulsed laser

Nanosecond pulsed laser induced photoporation has gained increasing attention from scholars as an effective method for delivering the membrane-impermeable extracellular materials into living cells. Compared with femtosecond laser, nanosecond laser has the advantage of high throughput and low costs. It also has a higher delivery efficiency than continuous wave laser. Here, we provide an extensive overview of current status of nanosecond pulsed laser induced photoporation, covering the photoporation mechanism as well as various factors that impact the delivery efficiency of photoporation. Additionally, we discuss various techniques for achieving photoporation, such as direct photoporation, nanoparticles-mediated photoporation and plasmonic substrates mediated photoporation. Among these techniques, nanoparticles-mediated photoporation is the most promising approach for potential clinical application. Studies have already been reported to safely destruct the vitreous opacities in vivo by nanosecond laser induced vapor nanobubble. Finally, we discuss the potential of nanosecond laser induced phototoporation for future clinical applications, particularly in the areas of skin and ophthalmic pathologies. We hope this review can inspire scientists to further improve nanosecond laser induced photoporation and facilitate its eventual clinical application.