Delineation of the pan-proteome of fish-pathogenic Streptococcus agalactiae strains using a label-free shotgun approach

Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network

Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, a novel Nucleotide-Level Deep Neural Network (NLDNN) is first proposed to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task, which takes DNA sequences as input and directly predicts experimental coverage values. Beyond predictive performance, it also assesses model performance by locating potential TF binding regions, discriminating TF-specific single-nucleotide polymorphisms (SNPs), and identifying causal disease-associated SNPs. The experimental results show that NLDNN outperforms the competing methods in these tasks. Then, a dual-path framework is designed for adversarial training of NLDNN to further improve the cross-species prediction performance by pulling the domain space of human and mouse species closer. Through comparison and analysis, it finds that adversarial training not only can improve the cross-species prediction performance between humans and mice but also enhance the ability to locate TF binding regions and discriminate TF-specific SNPs. By visualizing the predictions, it is figured out that the framework corrects some mispredictions by amplifying the coverage values of incorrectly predicted peaks.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Characterization and virulence of Streptococcus agalactiae deficient in SaeRS of the two-component system

There are a variety of regulatory systems in bacteria, among which the two-component system (TCS) can sense external environmental changes and make a series of physiological and biochemical reactions, which is crucial for the life activities of bacteria. As a member of TCS, SaeRS is considered to be an important virulence factor in Staphylococcus aureus, but its function in tilapia (Oreochromis niloticus)-derived Streptococcus agalactiae remains unknown. To explore the role of SaeRS in regulating virulence in the two-component system (TCS) of S. agalactiae from tilapia, ΔSaeRS mutant strain and CΔSaeRS complementary strain were constructed by homologous recombination. The results showed that the abilities of growth and biofilm formation of ΔSaeRS strain were significantly decreased when cultured in a brain heart infusion (BHI) medium (P < 0.01). Also, the survival rate of the ΔSaeRS strain in blood was decreased when compared with the wild strain S. agalactiae THN0901. Under the higher infection dose, the accumulative mortality of tilapia caused by the ΔSaeRS strain was significantly decreased (23.3%), of which THN0901 and CΔSaeRS strains were 73.3%. The results of competition experiments in tilapia showed that the invasion and colonization abilities of the ΔSaeRS strain were also dramatically lower than those of the wild strain (P < 0.01). Compared with the THN0901, the mRNA expression levels of virulence factors (fbsB, sip, cylE, bca, etc.) in the ΔSaeRS strain were significantly down-regulated (P < 0.01). SaeRS is one of the virulence factors of S. agalactiae. It plays a role in promoting host colonization and achieving immune evasion during the infection of tilapia, which provides a basis for exploring the pathogenic mechanism of S. agalactiae infected with tilapia.

Comparative proteomics and secretomics revealed virulence, and coresistance-related factors in non O1/O139 Vibrio cholerae recovered from 16 species of consumable aquatic animals

Vibrio cholerae can cause pandemic cholera in humans. The bacterium resides in aquatic environments worldwide. Identification of risk factors of V. cholerae in aquatic products is imperative for assuming food safety. In this study, we determined virulence-associated genes, cross-resistance between antibiotics and heavy metals, and genome fingerprinting profiles of non O1/O139 V. cholerae isolates (n = 20) recovered from 16 species of consumable aquatic animals. Secretomes and proteomes of V. cholerae with distinct genotypes and phenotypes were obtained by using two-dimensional gel electrophoresis (2D-GE) and/or liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques. Comparative secretomic analysis revealed 4 common and 45 differential extracellular proteins among 20 V. cholerae strains, including 13 virulence- and 8 resistance-associated proteins. A total of 21,972 intracellular proteins were identified, and comparative proteomic analysis revealed 215 common and 913 differential intracellular proteins, including 22 virulence- and 8 resistance-associated proteins. Additionally, different secretomes and proteomes were observed between V. cholerae isolates of fish and shellfish origins. A number of novel proteins with unknown function and strain-specific proteins were also discovered in the V. cholerae isolates. SIGNIFICANCE: V. cholerae can cause pandemic cholera in humans. The bacterium is distributed in aquatic environments worldwide. Identification of risk factors of V. cholerae in aquatic products is imperative for assuming food safety. Non-O1/O139 V. cholerae has been reported to cause sporadic cholera-like diarrhea and bacteremia diseases, which indicates virulence factors rather than the major cholera toxin (CT) exist. This study for the first time investigated proteomes and secretomes of non-O1/O139 V. cholerae originating from aquatic animals. This resulted in the identification of a number of virulence and coresistance-related factors, as well as novel proteins and strain-specific proteins in V. cholerae isolates recovered from 16 species of consumable aquatic animals. These results fill gaps for better understanding of pathogenesis and resistance of V. cholerae, and also support the increasing need for novel diagnosis and vaccine targets against the leading waterborne pathogen worldwide.

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Dichloromethane Degradation Pathway from Unsequenced Hyphomicrobium sp. MC8b Rapidly Explored by Pan-Proteomics

Several bacteria are able to degrade the major industrial solvent dichloromethane (DCM) by using the conserved dehalogenase DcmA, the only system for DCM degradation characterised at the sequence level so far. Using differential proteomics, we rapidly identified key determinants of DCM degradation for Hyphomicrobium sp. MC8b, an unsequenced facultative methylotrophic DCM-degrading strain. For this, we designed a pan-proteomics database comprising the annotated genome sequences of 13 distinct Hyphomicrobium strains. Compared to growth with methanol, growth with DCM induces drastic changes in the proteome of strain MC8b. Dichloromethane dehalogenase DcmA was detected by differential pan-proteomics, but only with poor sequence coverage, suggesting atypical characteristics of the DCM dehalogenation system in this strain. More peptides were assigned to DcmA by error-tolerant search, warranting subsequent sequencing of the genome of strain MC8b, which revealed a highly divergent set of dcm genes in this strain. This suggests that the dcm enzymatic system is less strongly conserved than previously believed, and that substantial molecular evolution of dcm genes has occurred beyond their horizontal transfer in the bacterial domain. Our study showed the power of pan-proteomics for quick characterization of new strains belonging to branches of the Tree of Life that are densely genome-sequenced.

Cytotoxicity of Streptococcus agalactiae secretory protein on tilapia cultured cells

Streptococcus agalactiae secrete virulence factors believed to be able of killing host tissues, especially under elevated water temperature. A direct effect of S. agalactiae secretory products on tilapia cells was tested on the tilapia kidney (TK-1) cell culture. The bacteria were cultured under four different temperature levels: 22, 29, 32 and 37°C; the cell-free portion was processed through SDS-PAGE; and distinct bands were identified by LC-MS/MS. At least, three virulence factors were identified, Bsp, PcsB and CAMP factor, with increasing levels as the cultured temperature rose. Expressions of bsp, pcsB and cfb were also up-regulated with the rising of the temperature in S. agalactiae culture. The supernatant from the bacteria cultured under specified temperatures was added into TK-1 cell-cultured wells. Morphological damage and mortality of the cultured cells, as determined by MTT method, were increased progressively from the supernatant treatment according to the rise of temperature in S. agalactiae culture. This study suggests that the production of the three virulence factors of S. agalactiae reported herein is temperature-dependent, and it is likely that CAMP factor directly kills the TK-1 cells since the other two types of protein are involved in S. agalactiae cell division and the bacterial adherence to host tissues.