pwc-archive/papers-with-abstracts huggingface dataset:
Joint optimization of replication potential and information storage set the letter size of primordial genetic alphabet
Authors: Hemachander Subramanian
Date: 2021-12-20 00:00:00

The simplest possible informational heteropolymer requires only a two-letter alphabet to be able to store information. The evolutionary choice of four monomers in the informational biomolecules RNA/DNA or their progenitors is intriguing, given the inherent difficulties in the simultaneous and localized prebiotic synthesis of all four monomers of progenitors of DNA from common precursors on early Earth. Excluding the scenario where a two-letter alphabet genome eventually expanded to include two more letters to code for more amino acids on teleological grounds, we show here that a heteropolymer sequence in the RNA-world-like scenario would have had to be composed of at least four letters in order to predictably fold into a specific secondary structure, and hence must have outcompeted the two-letter alphabet genomes. Using a model that we previously used to demonstrate the evolutionary advantages of unidirectional replication and anti-parallel strand orientation of duplex DNA, we show here that the competing constraints of maximum replicative potential and predictable secondary structure formation can be simultaneously satisfied only by palindromic heteropolymer sequences composed of a minimum of four letters, within the premise of the presence of sequence-dependent asymmetric cooperativity in these RNA/DNA progenitors.
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions
Authors: Duc Anh Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Date: 2021-12-15 00:00:00

Predicting drug-drug interactions (DDI) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e. side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels. State-of-the-art methods for this problem are graph neural networks (GNNs), which leverage neighborhood information in the graph to learn node representations. For DDI, however, there are many labels with complicated relationships due to the nature of side effects. Usual GNNs often fix labels as one-hot vectors that do not reflect label relationships and potentially do not obtain the highest performance in the difficult cases of infrequent labels. In this paper, we formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for drugs and one node for a label. We then present CentSmoothie, a hypergraph neural network that learns representations of nodes and labels altogether with a novel central-smoothing formulation. We empirically demonstrate the performance advantages of CentSmoothie in simulations as well as real datasets.
Deciphering antibody affinity maturation with language models and weakly supervised learning
Authors: Jeffrey A. Ruffolo, Jeffrey J. Gray, Jeremias Sulam
Date: 2021-12-14 00:00:00
Tasks: Language Modeling, Language Modelling, Multiple Instance Learning, Weakly-supervised Learning

In response to pathogens, the adaptive immune system generates specific antibodies that bind and neutralize foreign antigens. Understanding the composition of an individual's immune repertoire can provide insights into this process and reveal potential therapeutic antibodies. In this work, we explore the application of antibody-specific language models to aid understanding of immune repertoires. We introduce AntiBERTy, a language model trained on 558M natural antibody sequences. We find that within repertoires, our model clusters antibodies into trajectories resembling affinity maturation. Importantly, we show that models trained to predict highly redundant sequences under a multiple instance learning framework identify key binding residues in the process. With further development, the methods presented here will provide new insights into antigen binding from repertoire sequences alone.
High Expression of CDK1 and NDC80 Predicts Poor Prognosis of Bladder Cancer
Authors: S. Sajedeh Mousavi, Mohammad Jalil Zorriehzahra
Date: 2021-12-25 00:00:00
Tasks: Prognosis

Background: Bladder cancer is the 10th most common cancer worldwide, and its prevalence is increasing, especially in developing countries. Objective: In the present study, we employed gene expression profiles from the GSE163209 data set in the GEO database to identify potential molecular and genetic markers in BC patients. Methods: The data set comprised 217 samples, with 113 stage Ta tumor tissue samples and 104 stage T1 tumor tissue samples. The top 766 genes were chosen. P.value<0.0001 and |logFC|=1 was used to change the cutoff criteria for defining DEGs. Moreover, the MCODE plugin and cytoHubba plugin were employed to produce a module and detect 20 hub genes in these DEGs. We used GO and KEGG pathway enrichment analyses to get a better understanding of these DEGs. Results: The KEGG pathway enrichment results indicated that the top genes were mainly involved: Systemic lupus erythematosus, Alcoholism, and Viral carcinogenesis. SLE activation in the renal glomeruli could explain the connection between this disease's route and bladder cancer, and according to our results and previous researches, heavy alcohol intake can increase the risk of BC in males and particular populations. Conclusion: According to our hub genes, we can consider CDK1 and NDC80 as bladder cancer biomarkers. Not much research has been done on the effect of this gene on bladder cancer.
Cell-in-cell structures are involved in the competition between cells in breast cancer
Authors: S. Sajedeh Mousavi, Sara Razi
Date: 2021-12-25 00:00:00
Tasks: Prognosis

Breast cancer is the most common cancer in women worldwide, and discovering the biomarkers of this disease became so vital nowadays and Cell in Cell structure could be one of them, and it may be used as an available proxy for tumor malignancy. (CICs) are unusual in that keep morphologically healthy cells within another cell. They are found in various human cancers and result from active cell-cell interaction, and it has different kinds. In this study, we analyzed the microarray data from GEO (GSE103865) to genetically evaluate CICs' incidence in samples obtained from breast cancer patients to understand the relationship between the rate of CIC and the prognosis of breast cancer. The preprocessing was performed using R software. The DAVID website was used to analyze gene ontology (GO) and Gene and Genome (KEGG) pathways. The protein-protein interactions (PPIs) of the obtained DEGs were assessed using the STRING website, and hub modules in Cytoscape and cytoHubba were screened. According to the results from analyzing the 20 hub genes, we understood that overexpression of our Top genes is effective in focal adhesion, ECM-receptor interaction, platelet activation and PI3K-Akt signaling pathway, which shows that changes in these pathways could be the reason the overexpression of CICs in breast cancer. These data and research by many others have uncovered various genes involved in CIC formation and have started to give us an idea of why they are formed and how they could contribute to breast cancer
Temporal epistasis inference from more than 3,500,000 SARS-CoV-2 Genomic Sequences
Authors: Hong-Li Zeng, Yue Liu, Vito Dichio, Erik Aurell
Date: 2021-12-24 00:00:00

We use Direct Coupling Analysis (DCA) to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling. We use full-length, high-quality genomes from the GISAID repository up to October 2021, in total over 3,500,000 genomes. We find that DCA terms are more stable over time than correlations, but nevertheless change over time as mutations disappear from the global population or reach fixation. Correlations are enriched for phylogenetic effects, and in particularly statistical dependencies at short genomic distances, while DCA brings out links at longer genomic distance. We discuss the validity of a DCA analysis under these conditions in terms of a transient Quasi-Linkage Equilibrium state. We identify putative epistatic interaction mutations involving loci in Spike.
Replication of SARS-CoV-2 mutation analysis suggests differences in per-protein mutation characteristics
Authors: William Svoboda, Brendan McManamon, Sara Schwartz
Date: 2021-12-14 00:00:00

The increasing spread of COVID-19, caused by the virus SARS-CoV-2, raises concerns about the extent to which mutations have occurred across the viral genome. We present a partial replication of an earlier 2021 study by Wang, R. et al. that determined the presence of four substrains and eleven top mutations in the United States. We analyze a portion of the authors' data set in order to recreate Figure S1 from the paper, recapitulating the same features observed in the original figure. We further generate a summary of mutation characteristics for each of the 26 named proteins and confirm the significance of the spike protein at roughly 24% of all recorded mutations. Our analysis suggests that additional factors may affect per-protein mutation rate besides protein length.
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes
Authors: Juan Rodriguez-Rivas, Giancarlo Croce, Maureen Muscat, Martin Weigt
Date: 2021-12-19 00:00:00

The emergence of new variants of SARS-CoV-2 is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, pre-existing to SARS-CoV-2, we build statistical models that do not only capture amino-acid conservation but more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (ROC AUC ~0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution, future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing
Authors: Atul Sharma, Pranjal Jain, Ashraf Mahgoub, Zihan Zhou, Kanak Mahadik, Somali Chaterji
Date: 2021-12-19 00:00:00
Tasks: GPU, Language Modelling

Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads; then, calculates the perplexity metric to evaluate the corrected reads for different parameter choices. Next, it finds the one that produces the highest alignment rate without using a reference genome. The fundamental intuition of our approach is that the perplexity metric is inversely correlated with the quality of the assembly after error correction. Results: First, we show that the best k-mer value can vary for different datasets, even for the same EC tool. Second, we show the gains of our LM using its component attention-based transformers. We show the model's estimation of the perplexity metric before and after error correction. The lower the perplexity after correction, the better the k-mer size. We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality. Additionally, we show that our attention-based models have significant runtime improvement for the entire pipeline -- 18X faster than previous works, due to parallelizing the attention mechanism and the use of JIT compilation for GPU inferencing.
Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies
Authors: Cosimo Lupo, Natanael Spisak, Aleksandra M. Walczak, Thierry Mora
Date: 2021-12-15 00:00:00

Affinity maturation is crucial for improving the binding affinity of antibodies to antigens. This process is mainly driven by point substitutions caused by somatic hypermutations of the immunoglobulin gene. It also includes deletions and insertions of genomic material known as indels. While the landscape of point substitutions has been extensively studied, a detailed statistical description of indels is still lacking. Here we present a probabilistic inference tool to learn the statistics of indels from repertoire sequencing data, which overcomes the pitfalls and biases of standard annotation methods. The model includes antibody-specific maturation ages to account for variable mutational loads in the repertoire. After validation on synthetic data, we applied our tool to a large dataset of human immunoglobulin heavy chains. The inferred model allows us to identify universal statistical features of indels in heavy chains. We report distinct insertion and deletion hotspots, and show that the distribution of lengths of indels follows a geometric distribution, which puts constraints on future mechanistic models of the hypermutation process.
Comparative study of the evolution of human cancer gene duplications across fish
Authors: Ciara Baines, Richard Meitern, Randel Kreitsberg, Tuul Sepp
Date: 2021-12-14 00:00:00

Comparative studies of cancer-related genes allow us to gain novel information about the evolution and function of these genes, but also to understand cancer as a driving force in biological systems and species life histories. So far, comparative studies of cancer genes have focused on mammals. Here, we provide the first comparative study of cancer-related gene copy number variation in fish. As fish are evolutionarily older and genetically more diverse than mammals, their tumour suppression mechanisms should not only include most of the mammalian mechanisms, but also reveal novel (but potentially phylogenetically older) previously undetected mechanisms. We have matched the sequenced genomes of 65 fish species from the Ensemble database with the cancer gene information from the COSMIC database. By calculating the number of gene copies across species using the Ensembl CAFE data (providing species trees for gene copy number counts), we were able to develop a novel, less resource demanding method for ortholog identification. Our analysis demonstrates a masked relationship with cancer-related gene copy number variation (CNV) and maximum lifespan in fish species, suggesting that higher tumour suppressor gene CNV lengthens and oncogene CNV shortens lifespan, when both traits are added to the model. Based on the correlation between tumour suppressor and oncogene CNV, we were able to show which species have more tumour suppressors in relation to oncogenes. It could therefore be suggested that these species have stronger genetic defences against oncogenic processes. Fish studies could yet be a largely unexplored treasure trove for understanding the evolution and ecology of cancer, by providing novel insights into the study of cancer and tumour suppression, in addition to the study of fish evolution, life-history trade-offs, and ecology.
PEACH Tree: A Multiple Sequence Alignment and Tree Display Tool for Epidemiologists
Authors: Jordan Douglas, David Welch
Date: 2021-12-12 00:00:00
Tasks: Epidemiology, Multiple Sequence Alignment

PEACH Tree is an easy-to-use, online tool for displaying multiple sequence alignments and phylogenetic trees side-by-side. PEACH Tree is powerful for rapidly tracing evolutionary and transmission histories by filtering invariant sites out of the display, and allowing samples to readily be filtered out of the display. These features, coupled with the ability to display epidemiological metadata, make the tool suitable for infectious disease epidemiology. PEACH Tree further enables much needed communication between the fields of genomics and infectious disease epidemiology, as exemplified by the COVID-19 pandemic.
Stochastic simulation algorithm for isotope-based dynamic flux analysis
Authors: Quentin Thommen, Julien Hurbain, Benjamin Pfeuty
Date: 2022-01-03 00:00:00

Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways. Overall, SSA constitute an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.
Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks
Authors: Hongsong Feng, Kaifu Gao, Dong Chen, Alfred J Robison, Edmund Ellsworth, Guo-Wei Wei
Date: 2022-01-01 00:00:00
Tasks: Drug Discovery

Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large of number deaths around the world. Despite many decades' effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involves many interacting proteins in the interactome. Among them, dopamine transporter (DAT), serotonin transporter (SERT), and norepinephrine transporter (NET) are three major targets. Each of these targets has a large protein-protein interaction (PPI) network which must be considered in the anti-cocaine addiction drug discovery. This work presents DAT, SERT, and NET interactome network-informed machine learning/deep learning (ML/DL) studies of cocaine addiction. We collect and analyze 61 protein targets out 460 proteins in the DAT, SERT, and NET PPI networks that have sufficient existing inhibitor datasets. Utilizing autoencoder and other ML algorithms, we build ML/DL models for these targets with 115,407 inhibitors to predict drug repurposing potentials and possible side effects. We further screen their absorption, distribution, metabolism, and excretion, and toxicity (ADMET) properties to search for nearly optimal leads for anti-cocaine addiction. Our approach sets up a systematic protocol for artificial intelligence (AI)-based anti-cocaine addiction lead discovery.
The causal interaction between the subnetworks of a complex network
Authors: X. San Liang
Date: 2021-12-28 00:00:00

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system, in order to investigate problems such as the effective connectivity between two brain regions, each with millions of neurons involved. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems (e.g., the human brain), and is expected to have applications in many real world problems in different disciplines such as neuroscience, climate science, fluid dynamics, financial economics, to name a few.
Coupled effects of channels and synaptic dynamics in stochastic modelling of healthy and Parkinson's-disease-affected brains
Authors: Thi Kim Thoa Thieu, Roderick Melnik
Date: 2021-12-24 00:00:00

Our brain is a complex information processing network in which the nervous system receives information from the environment to quickly react to incoming events or learns from experience to sharp our memory. In the nervous system, the brain states translate collective activities of neurons interconnected via synaptic connections. In this paper, we study coupled effects of channels and synaptic dynamics under the stochastic influence of healthy brain cells with applications to Parkinson's disease (PD). In particular, we investigate the effects of random inputs in a subthalamic nucleus (STN) cell membrane potential model. The STN bursting phenomena and parkinsonian hypokinetic motor symptoms are closely connected, as electrical and chemical maneuvers modulating STN bursts are sufficient to ameliorate or mimic parkinsonian motor deficits. Deep brain stimulation (DBS) of the STN is an important surgical technique used in the treatment to improve PD symptoms. Our numerical results show that the random inputs strongly affect the spiking activities of the STN neuron not only in the case of healthy cells but also in the case of PD cells in the presence of DBS treatment. Specifically, the existence of a random refractory period together with random input current in the system may substantially influence an increased irregularity of spike trains of the output neurons.
Generative Models of Brain Dynamics -- A review
Authors: Mahta Ramezanian Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti, Irina Rish, Guillaume Dumas
Date: 2021-12-22 00:00:00

The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.