Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma.

Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.

Next-Generation Sequencing of T and B Cell Receptor Repertoires from COVID-19 Patients Showed Signatures Associated with Severity of Disease.

We profiled adaptive immunity in COVID-19 patients with active infection or after recovery and created a repository of currently >14 million B and T cell receptor (BCR and TCR) sequences from the blood of these patients. The B cell response showed converging IGHV3-driven BCR clusters closely associated with SARS-CoV-2 antibodies. Clonality and skewing of TCR repertoires were associated with interferon type I and III responses, early CD4+ and CD8+ T cell activation, and counterregulation by the co-receptors BTLA, Tim-3, PD-1, TIGIT, and CD73. Tfh, Th17-like, and nonconventional (but not classical antiviral) Th1 cell polarizations were induced. SARS-CoV-2-specific T cell responses were driven by TCR clusters shared between patients with a characteristic trajectory of clonotypes and traceability over the disease course. Our data provide fundamental insight into adaptive immunity to SARS-CoV-2 with the actively updated repository providing a resource for the scientific community urgently needed to inform therapeutic concepts and vaccine development.

Generating neuronal diversity in the mammalian cerebral cortex.

The neocortex is the part of the brain responsible for execution of higher-order brain functions, including cognition, sensory perception, and sophisticated motor control. During evolution, the neocortex has developed an unparalleled neuronal diversity, which still remains partly unclassified and unmapped at the functional level. Here, we broadly review the structural blueprint of the neocortex and discuss the current classification of its neuronal diversity. We then cover the principles and mechanisms that build neuronal diversity during cortical development and consider the impact of neuronal class-specific identity in shaping cortical connectivity and function.

Exploring membrane asymmetry and its effects on membrane proteins.

Plasma membranes utilize free energy to maintain highly asymmetric, non-equilibrium distributions of lipids and proteins between their two leaflets. In this review we discuss recent progress in quantitative research enabled by using compositionally controlled asymmetric model membranes. Both experimental and computational studies have shed light on the nuanced mechanisms that govern the structural and dynamic coupling between compositionally distinct bilayer leaflets. This coupling can increase the membrane bending rigidity and induce order - or lipid domains - across the membrane. Furthermore, emerging evidence indicates that integral membrane proteins not only respond to asymmetric lipid distributions but also exhibit intriguing asymmetric properties themselves. We propose strategies to advance experimental research, aiming for a deeper, quantitative understanding of membrane asymmetry, which carries profound implications for cellular physiology.

Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task.

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

Comparing full variation profile analysis with the conventional consensus method in SARS-CoV-2 phylogeny.

This study proposes a novel approach to studying severe acute respiratory syndrome coronavirus 2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position, forming a genomic matrix. Utilizing simulated short reads from genomes with specified mutations, we contrasted our genomic matrix approach with the consensus sequence method. Our matrix methodology, across multiple simulated datasets, accurately reflected the known mutations with an average accuracy improvement of 20% over the consensus method. In real-world tests using data from GISAID and NCBI-SRA, our approach demonstrated an increase in reliability by reducing the error margin by approximately 15%. The genomic matrix approach offers a more accurate representation of the viral genomic diversity, thereby providing superior insights into virus evolution and epidemiology.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.

LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing.

Single-cell sequencing has revolutionized our ability to dissect the heterogeneity within tumor populations. In this study, we present LoRA-TV (Low Rank Approximation with Total Variation), a novel method for clustering tumor cells based on the read depth profiles derived from single-cell sequencing data. Traditional analysis pipelines process read depth profiles of each cell individually. By aggregating shared genomic signatures distributed among individual cells using low-rank optimization and robust smoothing, the proposed method enhances clustering performance. Results from analyses of both simulated and real data demonstrate its effectiveness compared with state-of-the-art alternatives, as supported by improvements in the adjusted Rand index and computational efficiency.

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large.

Whole-soil-profile warming does not change microbial carbon use efficiency in surface and deep soils.

The paucity of investigations of carbon (C) dynamics through the soil profile with warming makes it challenging to evaluate the terrestrial C feedback to climate change. Soil microbes are important engines driving terrestrial biogeochemical cycles; their carbon use efficiency (CUE), defined as the proportion of metabolized organic C allocated to microbial biomass, is a key regulator controlling the fate of soil C. It has been theorized that microbial CUE should decline with warming; however, empirical evidence for this response is scarce, and data from deeper soils are particularly scarce. Here, based on soil samples from a whole-soil-profile warming experiment (0 to 1 m, +4 °C) and 18O tracing approach, we examined the vertical variation of microbial CUE and its response to ~3.3-y experimental warming in an alpine grassland on the Qinghai-Tibetan Plateau. Microbial CUE decreased with soil depth, a trend that was primarily controlled by soil C availability. However, warming had limited effects on microbial CUE regardless of soil depth. Similarly, warming had no significant effect on soil C availability, as characterized by extractable organic C, enzyme-based lignocellulose index, and lignin phenol-based ratios of vanillyls, syringyls, and cinnamyls. Collectively, our work suggests that short-term warming does not alter microbial CUE in either surface or deep soils, and emphasizes the regulatory role of soil C availability on microbial CUE.

Combining SARS-CoV-2 interferon-gamma release assay with humoral response assessment to define immune memory profiles.

OBJECTIVES: In the post-SARS-CoV-2 pandemic era, "breakthrough infections" are still documented, due to variants of concerns (VoCs) emergence and waning humoral immunity. Despite widespread utilization, the definition of the anti-Spike (S) immunoglobulin-G (IgG) threshold to define protection has unveiled several limitations. Here, we explore the advantages of incorporating T-cell response assessment to enhance the definition of immune memory profile. METHODS: SARS-CoV-2 interferon-gamma release assay test (IGRA) was performed on samples collected longitudinally from immunocompetent healthcare workers throughout their immunization by infection and/or vaccination, anti-receptor-binding domain IgG levels were assessed in parallel. The risk of symptomatic infection according to cellular/humoral immune capacities during Omicron BA.1 wave was then estimated. RESULTS: Close to 40% of our samples were exclusively IGRA-positive, largely due to time elapsed since their last immunization. This suggests that individuals have sustained long-lasting cellular immunity, while they would have been classified as lacking protective immunity based solely on IgG threshold. Moreover, the Cox regression model highlighted that Omicron BA.1 circulation raises the risk of symptomatic infection while increased anti-receptor-binding domain IgG and IGRA levels tended to reduce it. CONCLUSION: The discrepancy between humoral and cellular responses highlights the significance of assessing the overall adaptive immune response. This integrated approach allows the identification of vulnerable subjects and can be of interest to guide antiviral prophylaxis at an individual level.

Fission yeast poly(A) polymerase active site mutation Y86D alleviates the rad24Δ asp1-H397A synthetic growth defect and up-regulates mRNAs targeted by MTREC and Mmi1.

Expression of fission yeast Pho1 acid phosphatase is repressed under phosphate-replete conditions by transcription of an upstream prt lncRNA that interferes with the pho1 mRNA promoter. lncRNA-mediated interference is alleviated by genetic perturbations that elicit precocious lncRNA 3'-processing and transcription termination, such as (i) the inositol pyrophosphate pyrophosphatase-defective asp1-H397A allele, which results in elevated levels of IP8, and (ii) absence of the 14-3-3 protein Rad24. Combining rad24Δ with asp1-H397A causes a severe synthetic growth defect. A forward genetic screen for SRA (Suppressor of Rad24 Asp1-H397A) mutations identified a novel missense mutation (Tyr86Asp) of Pla1, the essential poly(A) polymerase subunit of the fission yeast cleavage and polyadenylation factor (CPF) complex. The pla1-Y86D allele was viable but slow-growing in an otherwise wild-type background. Tyr86 is a conserved active site constituent that contacts the RNA primer 3' nt and the incoming ATP. The Y86D mutation elicits a severe catalytic defect in RNA-primed poly(A) synthesis in vitro and in binding to an RNA primer. Yet, analyses of specific mRNAs indicate that poly(A) tails in pla1-Y86D cells are not different in size than those in wild-type cells, suggesting that other RNA interactors within CPF compensate for the defects of isolated Pla1-Y86D. Transcriptome profiling of pla1-Y86D cells revealed the accumulation of multiple RNAs that are normally rapidly degraded by the nuclear exosome under the direction of the MTREC complex, with which Pla1 associates. We suggest that Pla1-Y86D is deficient in the hyperadenylation of MTREC targets that precedes their decay by the exosome.

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile.

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).

HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses.

Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power.

Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.

Early detection and stratification of colorectal cancer using plasma cell-free DNA fragmentomic profiling.

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC.

Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells.

During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.

Analysis of secreted small extracellular vesicles from activated human microglial cell lines reveals distinct pro- and anti-inflammatory proteomic profiles.

Microglia are a specialized population of innate immune cells located in the central nervous system. In response to physiological and pathological changes in their microenvironment, microglia can polarize into pro-inflammatory or anti-inflammatory phenotypes. A dysregulation in the pro-/anti-inflammatory balance is associated with many pathophysiological changes in the brain and nervous system. Therefore, the balance between microglia pro-/anti-inflammatory polarization can be a potential biomarker for the various brain pathologies. A non-invasive method of detecting microglia polarization in patients would have promising clinical applications. Here, we perform proteomic analysis of small extracellular vesicles (sEVs) derived from microglia cells to identify sEVs biomarkers indicative of pro-inflammatory and anti-inflammatory phenotypic changes. sEVs were isolated from microglia cell lines under different inflammatory conditions and analyzed by proteomics by liquid chromatography with mass spectrometry. Our findings provide the potential roles of sEVs that could be related to the pathogenesis of various brain diseases.

A random forest model for predicting exosomal proteins using evolutionary information and motifs.

Non-invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence-based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif-based and ML-based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.