A natural language processing algorithm accurately classifies steatotic liver disease pathology to estimate the risk of cirrhosis.

BACKGROUND: Histopathology remains the gold standard for diagnosing and staging metabolic dysfunction-associated steatotic liver disease (MASLD). The feasibility of studying MASLD progression in electronic medical records based on histological features is limited by the free-text nature of pathology reports. Here we introduce a natural language processing (NLP) algorithm to automatically score MASLD histology features. METHODS: From the Mass General Brigham health care system electronic medical record, we identified all patients (1987-2021) with steatosis on index liver biopsy after excluding excess alcohol use and other etiologies of liver disease. An NLP algorithm was constructed in Python to detect steatosis, lobular inflammation, ballooning, and fibrosis stage from pathology free-text and manually validated in >1200 pathology reports. Patients were followed from the index biopsy to incident decompensated liver disease accounting for covariates. RESULTS: The NLP algorithm demonstrated positive and negative predictive values from 93.5% to 100% for all histologic concepts. Among 3134 patients with biopsy-confirmed MASLD followed for 20,604 person-years, rates of the composite endpoint increased monotonically with worsening index fibrosis stage (p for linear trend <0.005). Compared to simple steatosis (incidence rate, 15.06/1000 person-years), the multivariable-adjusted HRs for cirrhosis were 1.04 (0.72-1.5) for metabolic dysfunction-associated steatohepatitis (MASH)/F0, 1.19 (0.92-1.54) for MASH/F1, 1.89 (1.41-2.52) for MASH/F2, and 4.21 (3.26-5.43) for MASH/F3. CONCLUSIONS: The NLP algorithm accurately scores histological features of MASLD from pathology free-text. This algorithm enabled the construction of a large and high-quality MASLD cohort across a multihospital health care system and disclosed an accelerating risk for cirrhosis based on the index MASLD fibrosis stage.

Chronic metabolic stress drives developmental programs and loss of tissue functions in non-transformed liver that mirror tumor states and stratify survival.

Under chronic stress, cells must balance competing demands between cellular survival and tissue function. In metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD/NASH), hepatocytes cooperate with structural and immune cells to perform crucial metabolic, synthetic, and detoxification functions despite nutrient imbalances. While prior work has emphasized stress-induced drivers of cell death, the dynamic adaptations of surviving cells and their functional repercussions remain unclear. Namely, we do not know which pathways and programs define cellular responses, what regulatory factors mediate (mal)adaptations, and how this aberrant activity connects to tissue-scale dysfunction and long-term disease outcomes. Here, by applying longitudinal single-cell multi -omics to a mouse model of chronic metabolic stress and extending to human cohorts, we show that stress drives survival-linked tradeoffs and metabolic rewiring, manifesting as shifts towards development-associated states in non-transformed hepatocytes with accompanying decreases in their professional functionality. Diet-induced adaptations occur significantly prior to tumorigenesis but parallel tumorigenesis-induced phenotypes and predict worsened human cancer survival. Through the development of a multi -omic computational gene regulatory inference framework and human in vitro and mouse in vivo genetic perturbations, we validate transcriptional (RELB, SOX4) and metabolic (HMGCS2) mediators that co-regulate and couple the balance between developmental state and hepatocyte functional identity programming. Our work defines cellular features of liver adaptation to chronic stress as well as their links to long-term disease outcomes and cancer hallmarks, unifying diverse axes of cellular dysfunction around core causal mechanisms.

SARS-CoV-2 viral liver aggregates and scarce parenchymal infection implicate systemic disease as a driver of abnormal liver function.

BACKGROUND: Liver function tests (LFTs) are elevated in >50% of hospitalized individuals infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), with increased enzyme levels correlating with a more severe COVID-19 course. Despite these observations, evaluations of viral presence within liver parenchyma and viral impact on liver function remain controversial. METHODS AND RESULTS: Our work is a comprehensive immunopathological evaluation of liver tissue from 33 patients with severe, and ultimately fatal, cases of SARS-CoV-2 infection. Coupled with clinical data, we reveal the absence of SARS-CoV-2 infection in cholangiocytes and hepatocytes despite dramatic systemic viral presence. Critically, we identify significant focal viral sinusoidal aggregates in 2/33 patients and single viral RNA molecules circulating in the hepatic sinusoids of 15/33 patients. Utilizing co-immunofluorescence, focal viral liver aggregates in patients with COVID-19 were colocalized to platelet and fibrin clots, indicating the presence of virus-containing sinusoidal microthrombi. Furthermore, this patient cohort, from the initial months of the COVID-19 pandemic, demonstrates a general downtrend of LFTs over the course of the study timeline and serves as a remarkable historical time point of unattenuated viral replication within patients. CONCLUSIONS: Together, our findings indicate that elevated LFTs found in our patient cohort are not due to direct viral parenchymal infection with SARS-CoV-2 but rather likely a consequence of systemic complications of COVID-19. This work aids in the clinical treatment considerations of patients with SARS-CoV-2 as therapies for these patients may be considered in terms of their direct drug hepatotoxity rather than worsening hepatic function due to direct infection.

Cell-to-cell variability in the propensity to transcribe explains correlated fluctuations in gene expression.

Random fluctuations in gene expression lead to wide cell-to-cell differences in RNA and protein counts. Most efforts to understand stochastic gene expression focus on local (intrinisic) fluctuations, which have an exact theoretical representation. However, no framework exists to model global (extrinsic) mechanisms of stochasticity. We address this problem by dissecting the sources of stochasticity that influence the expression of a yeast heat shock gene, SSA1. Our observations suggest that extrinsic stochasticity does not influence every step of gene expression, but rather arises specifically from cell-to-cell differences in the propensity to transcribe RNA. This led us to propose a framework for stochastic gene expression where transcription rates vary globally in combination with local, gene-specific fluctuations in all steps of gene expression. The proposed model better explains total expression stochasticity than the prevailing ON-OFF model and offers transcription as the specific mechanism underlying correlated fluctuations in gene expression.

A computational framework for analyzing stochasticity in gene expression.

Stochastic fluctuations in gene expression give rise to distributions of protein levels across cell populations. Despite a mounting number of theoretical models explaining stochasticity in protein expression, we lack a robust, efficient, assumption-free approach for inferring the molecular mechanisms that underlie the shape of protein distributions. Here we propose a method for inferring sets of biochemical rate constants that govern chromatin modification, transcription, translation, and RNA and protein degradation from stochasticity in protein expression. We asked whether the rates of these underlying processes can be estimated accurately from protein expression distributions, in the absence of any limiting assumptions. To do this, we (1) derived analytical solutions for the first four moments of the protein distribution, (2) found that these four moments completely capture the shape of protein distributions, and (3) developed an efficient algorithm for inferring gene expression rate constants from the moments of protein distributions. Using this algorithm we find that most protein distributions are consistent with a large number of different biochemical rate constant sets. Despite this degeneracy, the solution space of rate constants almost always informs on underlying mechanism. For example, we distinguish between regimes where transcriptional bursting occurs from regimes reflecting constitutive transcript production. Our method agrees with the current standard approach, and in the restrictive regime where the standard method operates, also identifies rate constants not previously obtainable. Even without making any assumptions we obtain estimates of individual biochemical rate constants, or meaningful ratios of rate constants, in 91% of tested cases. In some cases our method identified all of the underlying rate constants. The framework developed here will be a powerful tool for deducing the contributions of particular molecular mechanisms to specific patterns of gene expression.

Thermodynamic state ensemble models of cis-regulation.

A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2) the binding constants that describe the affinity of the protein-protein and protein-DNA interactions that occur in each state, and (3) whether each state is capable of transcribing. Using these components, we demonstrate how to compute a cis-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own cis-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of cis-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial cis-regulation and in generating formalisms that accurately predict gene expression from DNA sequence.

Sequential turnover of human immunodeficiency virus type 1 env throughout the course of infection.

We examined the rates of variant population turnover of the V1-V2 and V4-V5 hypervariable domains of the human immunodeficiency virus type 1 (HIV-1) gp120 molecule in longitudinal plasma samples from 14 men with chronic HIV-1 infection using heteroduplex tracking assays (HTA). Six men had high rates of CD4+ T-cell loss, and eight men had low rates of CD4+ T-cell loss over 2.5 to 8 years of infection. We found that V1-V2 and V4-V5 env populations changed dramatically over time in all 14 subjects; the changes in these regions were significantly correlated with each another over time. The subjects with rapid CD4 loss had significantly less change in their env populations than the subjects with slow CD4 loss. The two subjects with rapid CD4 loss and sustained low CD4 counts (<150/microl for at least 2 years) showed stabilization of their V1-V2 and V4-V5 populations as reflected by low levels of total change in HTA pattern and low HTA indices (a novel measure of the emergence of new bands and band distribution); this stabilization was not observed in other subjects. The stabilization of env variant populations at low CD4 counts following periods of rapid viral evolution suggests that selective pressure on env, likely from new immune responses, is minimal when CD4 counts drop dramatically and remain low for extended periods of time.